Johann Christoph Gottscheds Briefwechsel - Historisch-kritische Ausgabe

This edition, containing approx. 6,000 letters written to and by Johann Christoph Gottsched (and his wife Luise Adelgune Viktoria Gottsched), documents for the first time the correspondence of one of the most significant supporters of the Early German Enlightenment. This volume presents the complete 25-volume historic-critical edition of letters by the Saxonian Academy of Sciences in Leipzig. Each letter has been meticulously edited and includes detailed commentary about the writer, the occasion of the correspondence, as well as information about names and realities. The historical and biographical “venue” of each letter is described in detail

The sand and gravel resources of the country around Terling, Essex : description of 1:25,000 resource sheet TL 71

Geological maps prepared by the Institute of Geological Sciences, data from 121 boreholes drilled during a feasibility study in 1966-67 and for the Mineral Assessment Unit in 1968- 69, and other pre-existing borehole information form the basis for the assessment of sand and gravel resources in the Terling area, Essex (1:25 000 sheet TL 71). The sheet is divided into resource blocks, each ideally containing 10 km2 of mineral (potentially workable sand and gravel). A simple statistical method has been applied to estimate the mineral volume in each block where at least five sample points are available. The reliability of the volume estimates is given at the 95 per cent confidence level. For each block the inferred area of mineral, the estimated average thickness of overburden and of mineral and the calculated mean grading of mineral samples are also given. The geology of the various deposits occurring in the sheet and details of each resource block are described. Borehole positions, the geology and topography, and mineral resource information are shown on the accompanying 1:25 000 map TL 71. Detailed borehole data are given

Lidar Remote Sensing of Forests: New Instruments and Modeling Capabilities

Lidar instruments provide scientists with the unique opportunity to characterize the 3D structure of forest ecosystems. This information allows us to estimate properties such as wood volume, biomass density, stocking density, canopy cover, and leaf area. Structural information also can be used as drivers for photosynthesis and ecosystem demography models to predict forest growth and carbon sequestration. All lidars use time-in-flight measurements to compute accurate ranging measurements; however, there is a wide range of instruments and data types that are currently available, and instrument technology continues to advance at a rapid pace. This seminar will present new technologies that are in use and under development at NASA for airborne and space-based missions. Opportunities for instrument and data fusion will also be discussed, as Dr. Cook is the PI for G-LiHT, Goddard's LiDAR, Hyperspectral, and Thermal airborne imager. Lastly, this talk will introduce radiative transfer models that can simulate interactions between laser light and forest canopies. Developing modeling capabilities is important for providing continuity between observations made with different lidars, and to assist the design of new instruments. Dr. Bruce Cook is a research scientist in NASA's Biospheric Sciences Laboratory at Goddard Space Flight Center, and has more than 25 years of experience conducting research on ecosystem processes, soil biogeochemistry, and exchange of carbon, water vapor and energy between the terrestrial biosphere and atmosphere. His research interests include the combined use of lidar, hyperspectral, and thermal data for characterizing ecosystem form and function. He is Deputy Project Scientist for the Landsat Data Continuity Mission (LDCM); Project Manager for NASA s Carbon Monitoring System (CMS) pilot project for local-scale forest biomass; and PI of Goddard's LiDAR, Hyperspectral, and Thermal (G-LiHT) airborne imager

The sand and gravel resources of the country around Eynsham, Oxfordshire : description of 1:25,000 resource sheet SP 40 and parts of SP 41

The geological maps of the Institute of Geological Sciences, pre-existing borehole information, and 78 boreholes drilled for the Mineral Assessment Unit form the basis of the assessment of sand and gravel resources in the Eynsham area, Oxfordshire. All deposits in the area which might be potentially workable for sand and gravel have been investigated and a simple statistical method has been used to estimate the volume. The reliability of the volume estimates is given at the symmetrical 95 per cent probability level. The 1 :25 000 map is divided into eight resource blocks containing between 3.8 and 11.2 km2 of potentially workable sand and gravel. For the blocks assessed statistically the geology of the deposits is described and the mineral-bearing area, the mean thickness of overburden and mineral, and the mean grading of the mineral are stated. Detailed borehole data are given. The geology, the position of the boreholes and the outlines of the resource blocks are shown on the accompanying map

The sand and gravel resources of the country around Maldon, Essex : description of 1:25,000 resource sheet TL 80

The geological maps of the Institute of Geological Sciences, pre-existing borehole information and seventy-three boreholes drilled specifically for assessment purposes (of which sixty-one were part of a feasibility study conducted in 1966-67 and twelve were drilled subsequently) form the basis of the assessment of sand and gravel resources in the Maldon area, Essex. All deposits in the area which might be potentially workable for sand and gravel (mineral ) have been investigated geologically and a simple statistical method has been used to estimate the volume. The reliability of the volume estimates is given at the 95 per cent confidence level. The 1:25 000 map is divided into resource blocks, each ideally containing approximately 10 km2 of sand and gravel. For each block the mineral bearing area, the mean thickness of overburden and mineral, and the mean grading are given and the geomorphology and geology of the deposits described. The position of the boreholes and exposures, the geology and topography and the outlines of the blocks are shown on the accompanying map TL 80. Detailed borehole data are given

Bibliometric studies on single journals: a review

This paper covers a total of 82 bibliometric studies on single journals (62 studies cover unique titles) published between 1998 and 2008 grouped into the following fields; Arts, Humanities and Social Sciences (12 items); Medical and Health Sciences (19 items); Sciences and Technology (30 items) and Library and Information Sciences (21 items). Under each field the studies are described in accordance to their geographical location in the following order, United Kingdom, United States and Americana, Europe, Asia (India, Africa and Malaysia). For each study, elements described are (a) the journal’s publication characteristics and indexation information; (b) the objectives; (c) the sampling and bibliometric measures used; and (d) the results observed. A list of journal titles studied is appended. The results show that (a)bibliometric studies cover journals in various fields; (b) there are several revisits of some journals which are considered important; (c) Asian and African contributions is high (41.4 of total studies; 43.5 covering unique titles), United States (30.4 of total; 31.0 on unique titles), Europe (18.2 of total and 14.5 on unique titles) and the United Kingdom (10 of total and 11 on unique titles); (d) a high number of bibliometrists are Indians and as such coverage of Indian journals is high (28 of total studies; 30.6 of unique titles); and (e) the quality of the journals and their importance either nationally or internationally are inferred from their indexation status

Applications using estimates of forest parameters derived from satellite and forest inventory data

From the combination of optical satellite data, digital map data, and forest inventory plot data, continuous estimates have been made for several forest parameters (wood volume, age and biomass). Five different project areas within Sweden are presented which have utilized these estimates for a range of applications. The method for estimating the forest parameters was a ”k-Nearest Neighbor” algorithm, which used a weighted mean value of k spectrally similar reference plots. Reference data were obtained from the Swedish National Forest Inventory. The output was continuous estimates at the pixel level for each of the variables estimated. Validation results show that accuracy of the estimates for all parameters was low at the pixel level (e.g., for total wood volume RMSE ranged from 58-80%), with a tendency toward the mean, and an underestimation of higher values while overestimating lower values. However, when the accuracy of the estimates is assessed over larger areas, the errors are lower, with best results being 10% RMSE over a 100 ha aggregation, and 17% RMSE over a 19 ha aggregation. Applications presented in this paper include moose and bird habitat studies, county level planning activities, use as input information to prognostic programs, and computation of statistics on timber volume within drainage basins and smaller land holdings. This paper provides a background on the kNN method and gives examples of how end users are currently applying satellite-produced estimation data such as these

Incorporating Physical Knowledge into Machine Learning for Planetary Space Physics

Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system. Machine learning can help scientists work with data on this larger scale. Unlike many applications of machine learning, a primary use in planetary space physics applications is to infer behavior about the system itself. This raises three concerns: first, the performance of the machine learning model, second, the need for interpretable applications to answer scientific questions, and third, how characteristics of spacecraft data change these applications. In comparison to these concerns, uses of black box or un-interpretable machine learning methods tend toward evaluations of performance only either ignoring the underlying physical process or, less often, providing misleading explanations for it. We build off a previous effort applying a semi-supervised physics-based classification of plasma instabilities in Saturn's magnetosphere. We then use this previous effort in comparison to other machine learning classifiers with varying data size access, and physical information access. We show that incorporating knowledge of these orbiting spacecraft data characteristics improves the performance and interpretability of machine learning methods, which is essential for deriving scientific meaning. Building on these findings, we present a framework on incorporating physics knowledge into machine learning problems targeting semi-supervised classification for space physics data in planetary environments. These findings present a path forward for incorporating physical knowledge into space physics and planetary mission data analyses for scientific discovery.Comment: 25 pages, 7 figures, accepted for publication in Frontiers in Astronomy and Space Sciences for the Research Topic of Machine Learning in Heliophysics at https://www.frontiersin.org/articles/10.3389/fspas.2020.0003