Alguns canvis en el concepte de traducció /

Versió revisada de la conferència pronunciada dins el II Seminari sobre la Traducció a Catalunya, celebrat a la Biblioteca-Museu Víctor Balaguer de Vilanova i la Geltrú, el 17 d'abril de 1993Conferència amb visió històrica sobre l'exercici de la traducció. En la part final de l'article es comenta la traducció al català de l''Alicia en terra de meravelles', de Carner, i de l''Odissea', de Riba

Erosion consequences on beach functions along the Maresme coast (NW Mediterranean, Spain)

A methodology to analyse the influence of erosion on beach functions at a regional scale is presented. The method considers erosion hazards at different timescales and assesses consequences by evaluating impacts on recreation and protection functions. To provide useful information to decision makers for managing these functions, hazard and consequences are integrated at the municipal level within a risk matrix. This methodology is applied at the Maresme, a 45-km sandy coast situated northward of Barcelona, which supports a strong urban and infrastructure development as well as an intensive beach recreational use. Obtained results indicate differentiated erosion implications along the region, depending on the management target considered. Thus, southern municipalities are more prone to erosion affecting the protection function of the beach and leisure use by the local population, whereas erosion will have a greater effect on foreign tourism in the northern municipalities. These results highlight the necessity to employ an articulated erosion risk assessment focusing on specific targets depending on the site in question. This methodology can help coastal managers to adopt tailored measures to manage erosion impacts towards specific goals, in a more efficient and sustainable manner

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

From vineyards to feedlots: a fund-flow scanning of sociometabolic transition in the Vallès County (Catalonia) 1860-1956-1999

We analyse the changes to agricultural metabolism in four municipalities of Vallès County (Catalonia, Iberia) by accounting for their agroecosystemfunds and flows during the socioecological transition from organic to industrial farming between the late nineteenth and twentieth centuries. The choice of three different stages in this transition allows us to observe the transformation of its funds and flows over time, the links established between them and the effect on their energy profiles.We emphasize the relevance of the integration and consistency of agroecosystem funds for energy efficiency in agriculture and their role as underlying historical drivers of this socioecological transition. While readjustment to market conditions and availability and affordability of external inputs are considered the main drivers of the transition, we also highlight the role of societal energy and nutritional transitions. An analysis of advanced organic agriculture c. 1860 reveals the great effort required to reproduce soil fertility and livestock from the internal recirculation of biomass. Meanwhile, a balance between land produce and livestock densities enabled the integration of funds, with a positive impact on energy performance. The adoption of fossil fuels and synthetic fertilizers c. 1956 reduced somewhat the pressure exerted on the land by overcoming the former dependence on local biomass flows to reproduce the agroecosystem. Yet external inputs diminished sustainability. Partial dependence on external markets existed congruently with internal crop diversity and the predominance of organic over industrial farm management. A shift towards animal production and consumption led to a new specialization process c. 1999 that resulted in crop homogenization and agroecological landscape disintegration. The energy returns of this linear feed-food livestock bioconversion declined compared to earlier mixed farming. Huge energy flows driven by a globalized economy ran through this agroecosystem, provoking deep impacts at both a local and external scale

A Net Energy Analysis of the Global Agriculture, Aquaculture, Fishing and Forestry System

The global agriculture, aquaculture, fishing and forestry (AAFF) energy system is subject to three unsustainable trends: (1) the approaching biophysical limits of AAFF; (2) the role of AAFF as a driver of environmental degradation; and (3) the long-term declining energy efficiency of AAFF due to growing dependence on fossil fuels. In response, we conduct a net energy analysis for the period 1971–2017 and review existing studies to investigate the global AAFF energy system and its vulnerability to the three unsustainable trends from an energetic perspective. We estimate the global AAFF system represents 27.9% of societies energy supply in 2017, with food energy representing 20.8% of societies total energy supply. We find that the net energy-return-on-investment (net EROI) of global AAFF increased from 2.87:1 in 1971 to 4.05:1 in 2017. We suggest that rising net EROI values are being fuelled in part by ‘depleting natures accumulated energy stocks’. We also find that the net energy balance of AAFF increased by 130% in this period, with at the same time a decrease in both the proportion of rural residents and also the proportion of the total population working in AAFF—which decreased from 19.8 to 10.3%. However, this comes at the cost of growing fossil fuel dependency which increased from 43.6 to 62.2%. Given the increasing probability of near-term fossil fuel scarcity, the growing impacts of climate change and environmental degradation, and the approaching biophysical limits of global AAFF, ‘Odum’s hoax’ is likely soon to be revealed

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets

Alguns canvis en el concepte de traducció /

Versió revisada de la conferència pronunciada dins el II Seminari sobre la Traducció a Catalunya, celebrat a la Biblioteca-Museu Víctor Balaguer de Vilanova i la Geltrú, el 17 d'abril de 1993Conferència amb visió històrica sobre l'exercici de la traducció. En la part final de l'article es comenta la traducció al català de l''Alicia en terra de meravelles', de Carner, i de l''Odissea', de Riba