Trazabilidad de la señal isotópica del oxígeno desde la lluvia a los espeleotemas en las cuevas de Ortigosa de Cameros (La Rioja, España)

[EN] A one-year monitoring survey has been carried out in La Paz and La Viña Caves in the Ortigosa de Cameros Cave System (NE Iberian Peninsula), in order to track the oxygen isotope signal from rainfall to speleothem calcite, assessing the ability of this signal to retain environmental information. Oxygen isotope signals of rainfall events, drip water —sampled every three months—, and speleothem calcite, precipitated over three-months, are compared. Water dripping follows precipitation events in winter, spring and summer, more closely in the near-surface drip points than in the deeper ones. In autumn, dripping is delayed with respect to rainfall, suggesting that water stays in the epikarst before dripping resumes after summer. This delay causes a deviation of the total drip water signal (average δ18O=−8.39‰ V-SMOW) from the rainfall signal (average δ18O=−7.41‰ V-SMOW). On the contrary, in winter the isotopic signal of drip water keeps the rainfall signal. Calcite isotopic signal (total average δ18O=−6.83‰ V-PDB) shows a small offset (0.62–0.75%) with respect to the signal predicted by drip water oxygen composition; this points to a limited kinetic effect in calcite precipitation, therefore calcite retains the signal of rainfall, especially in winter.[ES] Durante un año se han monitorizado las cuevas de La Paz y La Viña en el Sistema de Cuevas de Ortigosa de Cameros (NE de la Península Ibérica) para rastrear la señal isotópica del oxígeno desde la lluvia a la calcita espeleotémica, y así valorar la capacidad de esta señal para conservar información medioambiental. Se han comparado las señales isotópicas del oxígeno de los eventos de la lluvia, el agua de goteo (muestreada trimestralmente) y la calcita espeleotémica, precipitada también durante cada trimestre. El goteo en las cuevas responde a la precipitación en invierno, primavera y verano, más estrechamente en los puntos más próximos a la superficie que en los profundos. En otoño hay un retraso entre la lluvia y el goteo, lo que sugiere que el agua permanece por un tiempo en el epikarst, antes de que se reanude el goteo después del verano. Este retraso provoca que la señal isotópica del agua de goteo (media total de δ18O=−8.39‰ V-SMOW) se desvíe de la señal de la lluvia (media de δ18O=−7,41‰ V-SMOW). Por el contrario, en invierno la señal isotópica del agua de goteo es muy semejante a la de la lluvia. La composición isotópica de la calcita espeleotémica (media total de δ18O=−6,83‰ V-PDB) presenta un pequeño desfase (0,62–0,75%) respecto al valor que le correspondería por la composición isotópica del agua de goteo; esto indica que los efectos cinéticos durante la precipitación de la calcita son limitados, por lo que ésta conserva la señal de la lluvia, especialmente en invierno.This study was mainly funded by the CGL2009-10455/BTE research project of the Ministry of Science and Innovation and FEDER and the Análisis de Cuencas Sedimentarias Continentales, Geotransfer, PaleoQ y Gemorfología y Cambio Global research groups of the Aragón Government. We are most grateful to Ma Angeles, Sara y Juan for their invaluable help. We also thank to the Ortigosa de Cameros town hall for the cave access. The Government of La Rioja is acknowledged by the meteorological data from Villoslada de Cameros station. We are indebted to the referees for their valuable comments.Peer reviewe

Solar influence and hydrological variability during the Holocene from a speleothem annual record (Molinos Cave, NE Spain)

© 2015 John Wiley & Sons Ltd. We present a multi-proxy approach to reconstructing Holocene climate conditions in northeastern Spain based on an excellent correlation among the lamina thickness, colour parameters and isotope (δ18O and δ13C) variations recorded in a speleothem. An age model constructed from five U/Th dates and annual lamina counting suggests that the uppermost 14.7 cm of the MO-7 stalagmite grew between 7.2 and 2.5 ka before present but experienced a growth hiatus from 4.9 to 4.3 ka. Three spectral analysis methods were applied to 11 time series. The results reveal common solar periodicities on decennial (Gleissberg cycle) and centennial (De Vries-Suess cycle) scales. The onset of Holocene carbonate precipitation in the MO-7 stalagmite appears to be associated with a cold, wet period, whereas the hiatus and the end of growth are related to warm, dry periods. This environmental trend fits well within the regional Holocene climate.This study is a contribution to the CTM2013-48639-C2-1-R (OPERA), CGL2009–10455 and HIDROPAST (CGL2010-16376) projects (Spanish Government-European Regional Development Fund), the UZ2014-CIE-04 project (University of Zaragoza), the GA-LC-030/2011 project (Aragón Government-La Caixa) and the E–28 and S-97 research groups (Aragón Government).Peer Reviewe

Structure des grands bassins glaciaires dans le nord de la péninsule ibérique: comparaison entre les vallées d'Andorre (Pyrénées orientales), du Gállego (Pyrénées centrales) et du Trueba (Chaîne Cantabrique).

Les grandes vallées glaciaires de la Péninsule Ibérique sont situées dans la chaîne pyrénéo-cantabrique, principalement dans le bassin de l'Èbre. Ainsi, les vallées d'Andorre, de la Noguera Pallaresa et de la haute vallée du Gállego, dans les Pyrénées, ont eu des appareils glaciaires longs de 42, 50 et 40 km respectivement. Dans les vallées du Sil (bassin du Miño) et du Trueba, dans la Chaîne Cantabrique, ils atteignaient 42 et 16,5 km (Serrano-Cañadas, 1996 ; Gómez-Ortiz et al., 2001 ; Turu & Peña, 2006a et b ; Redondo-Vega et al., 2006). L'une des caractéristiques géomorphologiques de la plupart de ces vallées est l'existence d'une dépression morphologique du substratum dans les parties moyennes et terminales, interprétée comme la conséquence de l'érosion glaciaire. Dans tous les cas, on observe une architecture litho-stratigraphique commune (Vilaplana & Casas, 1983 ; Bordonau et al., 1989 ; Bordonau, 1992 ; Turu et al., 2002) représentée par trois unités géoélectriques : une unité inférieure très épaisse, avec des résistivités électriques basses (70 - 200 Ohms par mètre), qui traduit la présence de matériaux fins considérés comme d'origine lacustre ; une unité intermédiaire, moins épaisse, avec des valeurs de résistivité plus élevées (400 - 800 Ohms par mètre), pouvant être interprétée comme un système fluvio-deltaïque pro-glaciaire et une unité géoélectrique supérieure, avec des valeurs de résistivité très variables (100 - 1500 Ohms par mètre), constituée de sédiments alluviaux subactuels. La comparaison des données de type géophysique et géomécanique (sismique à réfraction et essais pressiométriques) montre que l'unité intermédiaire, considérée comme d'origine fluvio-deltaïque, présente des valeurs de vitesse sismique anormalement élevées, ainsi que de hautes valeurs de consolidation. Cette observation effectuée pour la première fois dans la vallée d'Andorre (Turu, 2000) montre des remarquables corrélations entre les hautes vitesses sismiques et les valeurs élevées de consolidation, ainsi que la très nette corrélation entre les hautes valeurs de consolidation et les tills sous-glaciaires. Elle permet d'interpréter l'unité intermédiaire comme essentiellement glaciaire et de remettre en question le modèle simple d'une séquence de comblement lacustre et deltaïque proposé jusqu´à maintenant

On the influence of model fragment properties on a machine learning-based approach for feature location

[EN] Context: Leveraging machine learning techniques to address feature location on models has been gaining attention. Machine learning techniques empower software product companies to take advantage of the knowledge and the experience to improve the performance of the feature location process. Most of the machine learning-based works for feature location on models report the machine learning techniques and the tuning parameters in detail. However, these works focus on the size and the distribution of the data sets, neglecting the properties of their contents. Objective: In this paper, we analyze the influence of three model fragment properties (density, multiplicity, and dispersion) on a machine learning-based approach for feature location. Method: The analysis of these properties is based on an industrial case provided by CAF, a worldwide provider of railway solutions. The test cases were evaluated through a machine learning technique that uses different subsets of a knowledge base to learn how to locate unknown features. Results: Results show that the density and dispersion properties have a direct impact on the results. In our case study, the model fragments with extra-small density values achieve results with up to 43% more precision, 41% more recall, 42% more F-measure, and 0.53 more Matthews Correlation Coefficient (MCC) than the model fragments with other density values. On the other hand, the model fragments with extra-small and small dispersion values achieve results with up to 53% more precision, 52% more recall, 52% more F-measure, and 0.57 more MCC than the model fragments with other dispersion values. Conclusions: The analysis of the results shows that both density and dispersion properties significantly influence the results. These results can serve not only to improve the reports by means of the model fragment properties, but also to be able to compare machine learning-based feature location approaches fairly improving the feature location results.This work has been partially supported by the Ministry of Economy and Competitiveness (MINECO), Spain through the Spanish National R+D+i Plan and ERDF funds under the Project ALPS (RTI2018096411-B-I00). We also thank the ITEA3 15010 REVaMP2 Project and ACIF/2018/171.Ballarin, M.; Marcén, AC.; Pelechano Ferragud, V.; Cetina, C. (2021). On the influence of model fragment properties on a machine learning-based approach for feature location. Information and Software Technology. 129:1-19. https://doi.org/10.1016/j.infsof.2020.106430S119129Marcén, A. C., Lapeña, R., Pastor, Ó., & Cetina, C. (2020). Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train control and management case. Journal of Systems and Software, 163, 110519. doi:10.1016/j.jss.2020.110519Pérez, F., Font, J., Arcega, L., & Cetina, C. (2019). Collaborative feature location in models through automatic query expansion. Automated Software Engineering, 26(1), 161-202. doi:10.1007/s10515-019-00251-9ZHUANG, X., ENGEL, B. A., LOZANO-GARCIA, D. F., FERNÁNDEZ, R. N., & JOHANNSEN, C. J. (1994). Optimization of training data required for neuro-classification. International Journal of Remote Sensing, 15(16), 3271-3277. doi:10.1080/01431169408954326Foody, G. M., & Mathur, A. (2004). A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(6), 1335-1343. doi:10.1109/tgrs.2004.827257Foody, G. M., Mathur, A., Sanchez-Hernandez, C., & Boyd, D. S. (2006). Training set size requirements for the classification of a specific class. Remote Sensing of Environment, 104(1), 1-14. doi:10.1016/j.rse.2006.03.004Weiss, G. M., & Provost, F. (2003). Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction. Journal of Artificial Intelligence Research, 19, 315-354. doi:10.1613/jair.1199Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 106, 249-259. doi:10.1016/j.neunet.2018.07.011Arcuri, A., & Fraser, G. (2013). Parameter tuning or default values? An empirical investigation in search-based software engineering. Empirical Software Engineering, 18(3), 594-623. doi:10.1007/s10664-013-9249-9Lapeña, R., Font, J., Pastor, Ó., & Cetina, C. (2017). Analyzing the impact of natural language processing over feature location in models. ACM SIGPLAN Notices, 52(12), 63-76. doi:10.1145/3170492.3136052Shabtai, A., Moskovitch, R., Elovici, Y., & Glezer, C. (2009). Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey. Information Security Technical Report, 14(1), 16-29. doi:10.1016/j.istr.2009.03.003Song, Q., Jia, Z., Shepperd, M., Ying, S., & Liu, J. (2011). A General Software Defect-Proneness Prediction Framework. IEEE Transactions on Software Engineering, 37(3), 356-370. doi:10.1109/tse.2010.90Cao, Z., Tian, Y., Le, T.-D. B., & Lo, D. (2018). Rule-based specification mining leveraging learning to rank. Automated Software Engineering, 25(3), 501-530. doi:10.1007/s10515-018-0231-zArcuri, A., & Briand, L. (2012). A Hitchhiker’s guide to statistical tests for assessing randomized algorithms in software engineering. Software Testing, Verification and Reliability, 24(3), 219-250. doi:10.1002/stvr.1486García, S., Fernández, A., Luengo, J., & Herrera, F. (2010). Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences, 180(10), 2044-2064. doi:10.1016/j.ins.2009.12.010Falessi, D., Di Penta, M., Canfora, G., & Cantone, G. (2016). Estimating the number of remaining links in traceability recovery. Empirical Software Engineering, 22(3), 996-1027. doi:10.1007/s10664-016-9460-6Jialei Wang, Peilin Zhao, Hoi, S. C. H., & Rong Jin. (2014). Online Feature Selection and Its Applications. IEEE Transactions on Knowledge and Data Engineering, 26(3), 698-710. doi:10.1109/tkde.2013.3

Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train control and management case

[EN] Traceability Link Recovery (TLR) has been a topic of interest for many years within the software engineering community. In recent years, TLR has been attracting more attention, becoming the subject of both fundamental and applied research. However, there still exists a large gap between the actual needs of industry on one hand and the solutions published through academic research on the other. In this work, we propose a novel approach, named Evolutionary Learning to Rank for Traceability Link Recovery (TLR-ELtoR). TLR-ELtoR recovers traceability links between a requirement and a model through the combination of evolutionary computation and machine learning techniques, generating as a result a ranking of model fragments that can realize the requirement. TLR-ELtoR was evaluated in a real-world case study in the railway domain, comparing its outcomes with five TLR approaches (Information Retrieval, Linguistic Rule-based, Feedforward Neural Network, Recurrent Neural Network, and Learning to Rank). The results show that TLR-ELtoR achieved the best results for most performance indicators, providing a mean precision value of 59.91%, a recall value of 78.95%, a combined F-measure of 62.50%, and a MCC value of 0.64. The statistical analysis of the results assesses the magnitude of the improvement, and the discussion presents why TLR-ELtoR achieves better results than the baselines.This work has been developed with the financial support of the Spanish State Research Agency and the Generalitat Valenciana under the projects DataME TIN2016-80811-P, ALPS RT12018-096411-B-100, ACIF/2018/171 and PROMETEO/2018/176, and co-financed with ERDF.Marcén, AC.; Lapeña, R.; Pastor López, O.; Cetina, C. (2020). Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train control and management case. Journal of Systems and Software. 163:1-24. https://doi.org/10.1016/j.jss.2020.110519S124163Abeles, P., 2017. Efficient Java Matrix Library. [Online; accessed 12-April-2017], http://ejml.org/.Antoniol, G., Canfora, G., Casazza, G., De Lucia, A., & Merlo, E. (2002). Recovering traceability links between code and documentation. IEEE Transactions on Software Engineering, 28(10), 970-983. doi:10.1109/tse.2002.1041053Antoniol, G., Cleland-Huang, J., Hayes, J. H., Vierhauser, M., 2017. Grand Challenges of Traceability: The Next Ten Years. arXiv:1710.03129.Arcuri, A., & Briand, L. (2012). A Hitchhiker’s guide to statistical tests for assessing randomized algorithms in software engineering. Software Testing, Verification and Reliability, 24(3), 219-250. doi:10.1002/stvr.1486Arcuri, A., & Fraser, G. (2013). Parameter tuning or default values? An empirical investigation in search-based software engineering. Empirical Software Engineering, 18(3), 594-623. doi:10.1007/s10664-013-9249-9Beleites, C., Neugebauer, U., Bocklitz, T., Krafft, C., & Popp, J. (2013). Sample size planning for classification models. Analytica Chimica Acta, 760, 25-33. doi:10.1016/j.aca.2012.11.007Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157-166. doi:10.1109/72.279181Cetina, C., Font, J., Arcega, L., & Pérez, F. (2017). Improving feature location in long-living model-based product families designed with sustainability goals. Journal of Systems and Software, 134, 261-278. doi:10.1016/j.jss.2017.09.022Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28. doi:10.1016/j.compeleceng.2013.11.024Dang, V., 2013. The Lemur Project - Wiki - RankLib. [Online; accessed April-2017], http://sourceforge.net/p/lemur/wiki/RankLib/.Davis, L., 1991. Handbook of Genetic Algorithms.Dyer, D., 2016. The Watchmaker Framework for Evolutionary Computation (Evolutionary/Genetic Algorithms for Java). [Online; accessed 7-April-2016], http://watchmaker.uncommons.org/.Falessi, D., Cantone, G., & Canfora, G. (2013). Empirical Principles and an Industrial Case Study in Retrieving Equivalent Requirements via Natural Language Processing Techniques. IEEE Transactions on Software Engineering, 39(1), 18-44. doi:10.1109/tse.2011.122Frakes, W. B., Baeza-Yates, R., 1992. Information Retrieval: Data Structures and Algorithms.García, S., Fernández, A., Luengo, J., & Herrera, F. (2010). Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences, 180(10), 2044-2064. doi:10.1016/j.ins.2009.12.010Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C., & Guisan, A. (2006). Evaluating the ability of habitat suitability models to predict species presences. Ecological Modelling, 199(2), 142-152. doi:10.1016/j.ecolmodel.2006.05.017Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366. doi:10.1016/0893-6080(89)90020-8Joachims, T., 1999. Svmlight: Support vector machine. SVM-Light Support Vector Machine http://svmlight. joachims. org/, University of Dortmund 19 (4).Kıraç, M. F., Aktemur, B., & Sözer, H. (2018). VISOR: A fast image processing pipeline with scaling and translation invariance for test oracle automation of visual output systems. Journal of Systems and Software, 136, 266-277. doi:10.1016/j.jss.2017.06.023Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse Processes, 25(2-3), 259-284. doi:10.1080/01638539809545028Lapeña, R., Font, J., Pastor, Ó., & Cetina, C. (2017). Analyzing the impact of natural language processing over feature location in models. ACM SIGPLAN Notices, 52(12), 63-76. doi:10.1145/3170492.3136052Lucia, A. D., Fasano, F., Oliveto, R., & Tortora, G. (2007). Recovering traceability links in software artifact management systems using information retrieval methods. ACM Transactions on Software Engineering and Methodology, 16(4), 13. doi:10.1145/1276933.1276934Mao, J., Xu, W., Yang, Y., Wang, J., Huang, Z., Yuille, A., 2014. Deep captioning with multimodal recurrent neural networks (M-RNN). arXiv:1412.6632.Meziane, F., Athanasakis, N., & Ananiadou, S. (2007). Generating Natural Language specifications from UML class diagrams. Requirements Engineering, 13(1), 1-18. doi:10.1007/s00766-007-0054-0Parizi, R. M., Lee, S. P., & Dabbagh, M. (2014). Achievements and Challenges in State-of-the-Art Software Traceability Between Test and Code Artifacts. IEEE Transactions on Reliability, 63(4), 913-926. doi:10.1109/tr.2014.2338254Piper, J. (1992). Variability and bias in experimentally measured classifier error rates. Pattern Recognition Letters, 13(10), 685-692. doi:10.1016/0167-8655(92)90097-jPoshyvanyk, D., Gueheneuc, Y.-G., Marcus, A., Antoniol, G., & Rajlich, V. (2007). Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval. IEEE Transactions on Software Engineering, 33(6), 420-432. doi:10.1109/tse.2007.1016Rempel, P., & Mader, P. (2017). Preventing Defects: The Impact of Requirements Traceability Completeness on Software Quality. IEEE Transactions on Software Engineering, 43(8), 777-797. doi:10.1109/tse.2016.2622264Rus, I., & Lindvall, M. (2002). Knowledge management in software engineering. IEEE Software, 19(3), 26-38. doi:10.1109/ms.2002.1003450Salton, G., McGill, M. J., 1986. Introduction to modern information retrieval.Shabtai, A., Moskovitch, R., Elovici, Y., & Glezer, C. (2009). Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey. Information Security Technical Report, 14(1), 16-29. doi:10.1016/j.istr.2009.03.003Song, Q., Jia, Z., Shepperd, M., Ying, S., & Liu, J. (2011). A General Software Defect-Proneness Prediction Framework. IEEE Transactions on Software Engineering, 37(3), 356-370. doi:10.1109/tse.2010.90SPANOUDAKIS, G., & ZISMAN, A. (2005). SOFTWARE TRACEABILITY: A ROADMAP. Handbook Of Software Engineering And Knowledge Engineering, 395-428. doi:10.1142/9789812775245_0014Spanoudakis, G., Zisman, A., Pérez-Miñana, E., & Krause, P. (2004). Rule-based generation of requirements traceability relations. Journal of Systems and Software, 72(2), 105-127. doi:10.1016/s0164-1212(03)00242-5Sundaram, S. K., Hayes, J. H., Dekhtyar, A., & Holbrook, E. A. (2010). 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Evaluating the benefits of empowering model-driven development with a machine learning classifier

[EN] Increasingly, the model driven engineering (MDE) community is paying more attention to the techniques offered by the machine learning (ML) community. This has led to the application of ML techniques to MDE related tasks in hope of increasing the current benefits of MDE. Nevertheless, there is a lack of empirical experiments that evaluate the benefits that ML brings to MDE. In this work, we evaluate the benefits of empowering model engineers of model-driven development (MDD) with an ML classifier. To do this, we tackled how to embed the ML classifier as part of the MDD. Then, this was evaluated using two different real industrial cases. Our results show that despite the ML part takes an extra effort, the use of the ML classifier pays off in terms of the quality results, the perceived usefulness, and intention to use.Generalitat Valenciana, Grant/Award Numbers: ACIF/2018/171, PROMETEO/2018/176; Ministerio de Ciencia, Innovacion y Universidades, Grant/Award Number: PID2021-128695OB-I00; Ministerio de Economia y Competitividad, Grant/Award Number: TIN2016-80811-P This work was supported in part by the Ministry of Economy and Competitiveness (MINECO) through the Spanish National R+D+i Plan and ERDF funds under the Project VARIATIVA under Grant PID2021-128695OB-I00, and in part by the Gobierno de Aragón (Spain) (Research Group S05_20D).Marcén, AC.; Pérez, F.; Pastor López, O.; Cetina, C. (2022). Evaluating the benefits of empowering model-driven development with a machine learning classifier. Software Practice and Experience. 52(11):2439-2455. https://doi.org/10.1002/spe.313324392455521

Geoquímica de elementos trazas en espeleotemas con laminación estacional de las cuevas de Ortigosa de Cameros (La Rioja)

[EN] The concentration of several trace elements (Mg, Ba, Sr,Al, Si and P) has been analyzed in two speleothems, corresponding to MIS-5 and MIS-1 according to U/Th dating, and one recent deposit, experimentally recorded.All them belong to La Paz cave in Ortigosa de Cameros (La Rioja). The analyzed trace element content displays a seasonality that agrees with the speleothem lamination.Trace element concentration is higher in the dark laminae, formed during the fall-winter season. This content is linked to the flushing into the cave of organic matter from soil activity, in fall, when precipitation resumes. Mg/Ca and Sr/Ca evolutions point to a decrease of humidity during MIS-5 with respect to MIS-1, whereas at present aridity increases, probably in relation to an enlarging of the seasonal contrast.[ES] Se estudia la concentración de varios elementos traza (Mg, Ba, Sr,Al, Si y P) en dos espeleotemas, datados por U/Th como MIS-5 y MIS-1, y en un depósito actual, de origen experimental, todos ellos de la cueva de La Paz en Ortigosa de Cameros (La Rioja). Los elementos analizados muestran una estacionalidad acorde con la laminación de los depósitos. La concentración de trazas es mayor en las láminas oscuras, correspondientes a otoño-invierno, asociada a la materia orgánica procedente de la actividad edáfica, que es introducida por las precipitaciones de otoño, tras la sequía estival. Las evoluciones de Mg/Ca y Sr/Ca indican una disminución de la humedad durante el MIS-5 respecto al MIS-1, mientras que en la actualidad hay un aumento de la aridez. Estas variaciones podrían estar influenciadas por modificaciones del contraste estacional.Este trabajo ha sido financiado por el proyecto CGL2009-10455/BTE (Ministerio de Ciencia e Innovación y Fondos Europeos). Agradecemos la colaboración del Ayuntamiento de Ortigosa de Cameros y de Mª Ángeles, Sara y Juan. Agradecemos también las sugerencias de la Dra. Ana Alonso Zarza en la revisión del manuscrito.Peer reviewe

Testing the reliability of detrital cave sediments as recorders of paleomagnetic secular variations, Seso Cave System (Central Pyrenees, Spain)

A paleomagnetic study has been carried out on a waterlaid detrital sedimentary sequence of ~240cm thick within the Seso Cave System (West-Central Pyrenees). In these sediments, seven charcoal samples were dated using 14C AMS ranging from 2080 to 650calyrBP (130BC-1300AD). Two levels of human occupation of the cave have been recognized by ceramics associated to the Iberian Period and to the Roman Period, respectively. The detrital sedimentary sequence is made of autochthonous (piping detached material from the Eocene marls host rock inside of the cavity) and allochthonous (stream transported sediments from the outside) sediments. The autochthonous material (first 100cm), made of fine grain laminated sediments (lutites and marls) corresponds to pond facies; the allochthonous material (190-240cm) is made of lutites and sands and corresponds to stream facies, and both facies are mixed from 100 to 190cm. The increase in sedimentation rate towards the end of the sequence (stream facies) points to an intensification of the alluvial activity as a possible consequence of a more arid climate during the Medieval Climate Anomaly. For the paleomagnetic study, 44 discrete cylindrical samples were taken along the detrital sequence. The values of the natural remanent magnetization and magnetic susceptibility are significantly lower in the pond sediments than in the stream sediments. The declination and inclination of the paleomagnetic characteristic component (sister samples analyzed by both alternating field and temperature demagnetizing procedure) of each depth point is compared to the Spanish archeomagnetic catalog and available geomagnetic models (ARCH3k.1, CALS3k.4, CALS10k.1b and SCHA.DIF.3K) in order to determine the accuracy of these sediments recording the Earth's magnetic field. Results suggest that these sediments poorly record the Earth's magnetic field, however, paleomagnetic inclination shows similar results between both demagnetizing methods and the inclination is well recorded especially in the younger stream facies. The lack of archeological remains with absolute dates from 925 to 1545calyrBP in the Iberian paleomagnetic secular variation reference curve has prevented, up to now, the study of that time period. Therefore, the inclination data from the Seso Cave deposit is the first record of the Iberian paleomagnetic secular variation during most part of the Medieval time, and they are closer to the inclination values of one geomagnetic model (CALS10k.1b). © 2014 Elsevier B.V.BOU acknowledges the JAEdoc Programme of CSIC, partly financed by the European Social Fund. All authors thank the financial support of the Instituto de Estudios Altoaragoneses, of the projects CGL2009-10455/BTE, HIDROPAST CGL2010-16376 (MICINN and FEDER), ORDESA (Autonomous Organism of National Parks), and the PaleoQ Group (Universidad de Zaragoza-Gobierno de Aragón). We are also indebted to Jaume Mas-Moiset and Xavier Fuertes from the Grup d'Espeleologia de Badalona (GEB) for the cartography of the cave system and field support. Dr. José Luis Peña-Monné and Dr. Fernando Pérez-Lambán are acknowledged for the classification and comments of pottery remnants. We are indebted to GEOMAGIA50, which is a database that comes with a web interface intended to give users easy access to archeomagnetic data (Donadini et al., 2006 and Korhonen et al., 2008). Comments and suggestions from two anonymous reviewers are deeply acknowledged.Peer Reviewe

Structure des grands bassins glaciaires dans le nord de la péninsule ibérique: comparaison entre les vallées d'Andorre (Pyrénées orientales), du Gállego (Pyrénées centrales) et du Trueba (Chaîne Cantabrique).

Les grandes vallées glaciaires de la Péninsule Ibérique sont situées dans la chaîne pyrénéo-cantabrique, principalement dans le bassin de l'Èbre. Ainsi, les vallées d'Andorre, de la Noguera Pallaresa et de la haute vallée du Gállego, dans les Pyrénées, ont eu des appareils glaciaires longs de 42, 50 et 40 km respectivement. Dans les vallées du Sil (bassin du Miño) et du Trueba, dans la Chaîne Cantabrique, ils atteignaient 42 et 16,5 km (Serrano-Cañadas, 1996 ; Gómez-Ortiz et al., 2001 ; Turu & Peña, 2006a et b ; Redondo-Vega et al., 2006). L'une des caractéristiques géomorphologiques de la plupart de ces vallées est l'existence d'une dépression morphologique du substratum dans les parties moyennes et terminales, interprétée comme la conséquence de l'érosion glaciaire. Dans tous les cas, on observe une architecture litho-stratigraphique commune (Vilaplana & Casas, 1983 ; Bordonau et al., 1989 ; Bordonau, 1992 ; Turu et al., 2002) représentée par trois unités géoélectriques : une unité inférieure très épaisse, avec des résistivités électriques basses (70 - 200 Ohms par mètre), qui traduit la présence de matériaux fins considérés comme d'origine lacustre ; une unité intermédiaire, moins épaisse, avec des valeurs de résistivité plus élevées (400 - 800 Ohms par mètre), pouvant être interprétée comme un système fluvio-deltaïque pro-glaciaire et une unité géoélectrique supérieure, avec des valeurs de résistivité très variables (100 - 1500 Ohms par mètre), constituée de sédiments alluviaux subactuels. La comparaison des données de type géophysique et géomécanique (sismique à réfraction et essais pressiométriques) montre que l'unité intermédiaire, considérée comme d'origine fluvio-deltaïque, présente des valeurs de vitesse sismique anormalement élevées, ainsi que de hautes valeurs de consolidation. Cette observation effectuée pour la première fois dans la vallée d'Andorre (Turu, 2000) montre des remarquables corrélations entre les hautes vitesses sismiques et les valeurs élevées de consolidation, ainsi que la très nette corrélation entre les hautes valeurs de consolidation et les tills sous-glaciaires. Elle permet d'interpréter l'unité intermédiaire comme essentiellement glaciaire et de remettre en question le modèle simple d'une séquence de comblement lacustre et deltaïque proposé jusqu´à maintenant