The Veldwezelt site (province of Limburg, Belgium): environmental and stratigraphical interpretations

2011 ◽  
Vol 90 (2-3) ◽  
pp. 73-94 ◽  
Author(s):  
E.P.M. Meijs

AbstractUphill and drainage line environments reveal many hiatuses or discordances, because of truncation by erosion. In downslope position accumulation often prevailed outside the drainage lines and prevented erosion, even during unstable periods. Consequently, downslope sections yield the most detailed environmental data, but often lack contact with uphill series. However, for stratigraphical correlation the contact between downslope and uphill series is essential. In the Veldwezelt loess sequence this contact is intact, which provides additional data on transitional processes. In view of these special palaeoenvironmental conditions, exhibiting a transition between downslope and uphill areas and a south-east trending stream, an extraordinarily detailed Late Saalian, Eemian and Weichselian loess sequence could be reconstructed. The Veldwezelt series furnished important pedological, sedimentological, faunal, tephrochronological and cryogenic data, on the basis of which palaeoenvironmental conclusions could be drawn and six types of pedo-sedimentological cycles distinguished. A stratigraphical overview was obtained by correlating the Veldwezelt section with other west European loess frameworks and tephra sequences; the sedimentary series at Harmignies (Mons Basin, southern Belgium) and the Greenland GRIP ice core.

Author(s):  
John Waller

Geographic outliers at GBIF (Global Biodiversity Information Facility) are a known problem. Outliers can be errors, coordinates with high uncertainty, or simply occurrences from an undersampled region. Often in data cleaning pipelines, outliers are removed (even if they are legitimate points) because the researcher does not have time to verify each record one-by-one. Outlier points are usually occurrences that need attention. Currently, there is no outlier detection implemented at GBIF and it is up to the user to flag outliers themselves. DBSCAN (a density-based algorithm for discovering clusters in large spatial databases with noise) is a simple and popular clustering algorithm. It uses two parameters, (1) distance and (2) a minimum number of points per cluster, to decide if something is an outlier. Since occurrence data can be very patchy, non-clustering distance-based methods will fail often Fig. 1. DBSCAN does not need to know the expected number of clusters in advance. DBSCAN does well using only distance and does not require some additional environmental variables like Bioclim. Advanatages of DBSCAN : Simple Easy to understand Only two parameters to set Scales well No additional data sources needed Users would understand how their data was changed Simple Easy to understand Only two parameters to set Scales well No additional data sources needed Users would understand how their data was changed Drawbacks : Only uses distance Must choose parameter settings Sensitive to sparse global sampling Does not include any other relevant environmental information Can only flag outliers outside of a point blob Only uses distance Must choose parameter settings Sensitive to sparse global sampling Does not include any other relevant environmental information Can only flag outliers outside of a point blob Outlier detection and error detection are different. If your goal is to produce a system with no false positives, it will fail. While more complex environmentally-informed outlier detection methods (like reverse jackknifing (Chapman 2005)) might perform better for certain examples or even in genreal, DBSCAN performs adequately on almost everything despite being very simple. Currently I am using DBSCAN to find errors and assess dataset quality. It is a Spark job written in Scala (github). It does not run on species with lots of (>30K) unique latitude-longitude points, since the current implementation relies on an in-memory distance matrix. However, around 99% of species (plants, animals, fungi) on GBIF have fewer than >30K unique lat-long points (2,283 species keys / 222,993 species keys). There are other implementations ( example) that might scale to many more points. There are no immediate plans to include DBSCAN outliers as a data quality flag on GBIF, but it could be done somewhat easily, since this type of method does not rely on any external environmental data sources and already runs on the GBIF cluster.


1973 ◽  
Vol 12 (02) ◽  
pp. 102-107 ◽  
Author(s):  
D. J. Protti ◽  
Nancy Craven ◽  
A. Naimark ◽  
R. M. Cherniack

A previously described comprehensive respiratory information system (CRIS) has been changed to introduce new spirometric tests which are sensitive to minor abnormalities, revise on the basis of additional data the regression equations which define normal values to various parameters of pulmonary function and refine the system’s interpretation scheme. The beneficial effects of transferring the system from a large IBM 360/65 to a small CDC 1700 are presented. An analysis of the costs of processing routine pulmonary function studies reveals that a 40°/o saving is realized when a computer is used in comparison to the use of the usual manual methods.


2015 ◽  
Vol 42 (2) ◽  
pp. 226-235 ◽  
Author(s):  
G. N. H. Waller

Eight species of mesoplodont whales (genus Mesoplodon Gervais, 1850) named during the nineteenth century are based on valid descriptions. A checklist with the original description and type material for each of these species is provided. Additional data given may include type locality and illustrative sources, type material holding institution and type registration number(s). The only type specimen for which a record of external morphology was published relates to the 1803 stranding of Sowerby's beaked whale (Mesoplodon bidens).


2018 ◽  
Vol 5 (2) ◽  
pp. 99-105
Author(s):  
Thanapauge Chamaratana ◽  
Thawatchai Sangseema

Abstract The tendency of migration of Lao workers to Thailand is likely to increase especially migration pattern is social network. The objective of this research was to study factors effecting the migration through social network of Lao workers in Udon Thani. Qualitative research method was applied in the study. Unit of analysis was group level. In-depth interview guideline was applied to collect data from 15 Laotian workers. The research site was Udon Thani, Thailand. Participatory observation and non-participatory observation were use for additional data collection. The ATLAS.ti programme was applied to categorize data, and data analysis was based on the content analysis method. The research results showed that the crucial push factors which contributed migration among Laotian workers included Udon Thani Unemployment in residency, and low revenue in residency and important pull factors include higher compensation, worker demand of establishments in Udon Thani province, Laotian employers' values in Udon Thani, and social network of Laotian workers in destination.


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