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Data from: Multi-perspective predictive modeling for acute kidney injury in general hospital populations using electronic medical records
负责人:
关键词:
predictive modeling;Acute Kidney Injury;Electronic medical record;Prediction;Machine Learning
DOI:
doi:10.5061/dryad.qc90qb4
摘要:
. Five machine learning methods were used to build prediction models. Prediction tasks from four clinical perspectives with different modeling
Data from: Out-of-sample predictions from plant–insect food webs: robustness to missing and erroneous trophic interaction records
负责人:
关键词:
novel interactions;food web;Lepidoptera;host-use model;Herbivory;trophic niche model;Holocene;plant-herbivore;introduced species;predictions
DOI:
doi:10.5061/dryad.5c1g6
摘要:
r encounter frequency in the field had little effect on the loss of predictive power. Host-use models may have immediate value for predicting novel interacti
Data from: Constructing predictive models of human running
负责人:
关键词:
human running;kinematics;motion capture
DOI:
doi:10.5061/dryad.r9v30
摘要:
in predictive models of human running with six additional states comprising the position and velocity of the swing-leg ankle. Our methods are general, and may be appli
Data from: Integrating correlation between traits improves spatial predictions of plant functional composition
负责人:
关键词:
generalized additive mixed effect models;Community weighted mean;response and effect traits
DOI:
doi:10.5061/dryad.v475g
摘要:
ents. Predictive approaches traditionally model the weighted mean trait values of communities (CWMs) as a function of environmental gradients. However, mo
Data from: Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform
负责人:
关键词:
Genotype x environment interaction;Predictive model;Exome Capture;Douglas-fir;Genomic selection;Full-sib families;Pseudotsuga menziesii Mirb. (Franco)
DOI:
doi:10.5061/dryad.vk048
摘要:
-site to single site) and time (age-age/ trait-trait). Results The RR-BLUP and GRR models produced similar predictive accuracies across the studi
Data from: How to predict biodiversity in space? An evaluation of modelling approaches in marine ecosystems
负责人:
关键词:
GDM;HMSC;Hill diversity profile;Random Forest;SSDM;?-diversity
DOI:
doi:10.5061/dryad.2fv7mt8
摘要:
h conceptual and technical aspects. There exist many predictive models, and an integrative evaluation can help understand their performance in handlin
Data from: Quantifying and modelling decay in forecast proficiency indicates the limits of transferability in land-cover classification
负责人:
关键词:
R;classification;2006-2010
DOI:
doi:10.5061/dryad.s156b
摘要:
e. 2. Following the concept of ‘forecast horizon’, we suggest that the predictability of land-cover classification models should be methodologically explored with quantitative
Data from: Where to start? development of a spatial tool to prioritise retrofitting of power line poles that are dangerous to raptors
负责人:
关键词:
Power line pole;Electrocution risk map;High-risk power poles;Predictive Modelling;Retrofitting activities;Boosted regression trees;Raptor electrocution
DOI:
doi:10.5061/dryad.4d620q0
摘要:
d the accuracy of the predictive models of raptor electrocution risk for large geographic areas. By incorporating the technical characteristics of the power poles
Data from: Trait-matching and phylogeny as predictors of predator-prey interactions involving ground beetles
负责人:
关键词:
networks;Matching-centrality formalism;food web;Diplopoda;Isopoda;Coleoptera;Carabidae;Pseudoscorpiones;Functional traits;Araneae;trophic interactions
DOI:
doi:10.5061/dryad.7tn01
摘要:
predictive ecological models that tested different combinations of all predator and prey functional traits, and phylogenetic information. The mo
Data from: How to evaluate community predictions without thresholding?
负责人:
关键词:
community modelling ;stacked species distribution models;validation;environmental gradient;insects;plants;S?rensen index;Jaccard index;null model
DOI:
doi:10.5061/dryad.8sf7m0ch5
摘要:
es by first modelling individual species and then stacking the modelled predictions to form assemblages. The evaluation of the predictive performance is usua

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