Consulta: subjectFacets:"Multinomial logistic regression"
Registros recuperados: 2
Data/hora: 16/06/2024 00:20:21

Digital elevation model quality on digital soil mapping prediction accuracy
Provedor de dados: 64
Autores: Costa,Elias Mendes; Samuel-Rosa,Alessandro; Anjos,Lúcia Helena Cunha dos.
ABSTRACT Digital elevation models (DEM) used in digital soil mapping (DSM) are commonly selected based on measures and indicators (quality criteria) that are thought to reflect how well a given DEM represents the terrain surface. The hypothesis is that the more accurate a DEM, the more accurate will be the DSM predictions. The objective of this study was to assess different criteria to identify the DEM that delivers the most accurate DSM predictions. A set of 10 criteria were used to evaluate the quality of nine DEMs constructed with different data sources, processing routines and three resolutions (5, 20, and 30 m). Multinomial logistic regression models were calibrated using 157 soil observations and terrain attributes derived from each DEM. Soil class...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Multinomial logistic regression; Predictor variables; Collinearity; Shannon entropy.
Ano: 2018
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-70542018000600608

AB_SA: Accessory genes-Based Source Attribution – tracing the source of Salmonella enterica Typhimurium environmental strains
Provedor de dados: 5
Autores: Guillier, Laurent; Gourmelon, Michele; Lozach, Solen; Cadel-six, Sabrina; Vignaud, Marie-léone; Munck, Nanna; Hald, Tine; Palma, Federica.
The partitioning of pathogenic strains isolated in environmental or human cases to their sources is challenging. The pathogens usually colonize multiple animal hosts, including livestock, which contaminate the food-production chain and the environment (e.g. soil and water), posing an additional public-health burden and major challenges in the identification of the source. Genomic data opens up new opportunities for the development of statistical models aiming to indicate the likely source of pathogen contamination. Here, we propose a computationally fast and efficient multinomial logistic regression source-attribution classifier to predict the animal source of bacterial isolates based on ‘source-enriched’ loci extracted from the accessory-genome profiles...
Tipo: Text
Palavras-chave: Environmental contamination; Multinomial logistic regression; Pangenome-wide enrichment analysis; Source attribution; Salmonella Typhimurium.
Ano: 2020
URL: https://archimer.ifremer.fr/doc/00624/73632/73072.pdf