{"id":"W2668423657","doi":"10.1002/ece3.3122","title":"Relative Selection Strength: Quantifying effect size in habitat‐ and step‐selection inference","year":2017,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":256,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"U.S. Geological Survey","keywords":"Covariate; Selection (genetic algorithm); Inference; Statistics; RSS; Context (archaeology); Statistical inference; Econometrics; Computer science; Mathematics; Ecology; Geography; Machine learning; Artificial intelligence; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1504866,0.0009887085,0.00183509,0.005087347,0.0007395155,0.002333667,0.00271466,0.002318909,0.005811019],"category_scores_gemma":[0.4301473,0.0007498855,0.003732153,0.004301842,0.005478458,0.004176758,0.003823832,0.003069177,0.0003859309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007614195,"about_ca_system_score_gemma":0.0006412116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001071317,"about_ca_topic_score_gemma":0.001083492,"domain_scores_codex":[0.8639144,0.1169568,0.005039946,0.007725843,0.005855451,0.0005076642],"domain_scores_gemma":[0.2279603,0.7425991,0.008140138,0.01781074,0.002960558,0.000529155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001924676,0.0003070226,0.3007307,0.003273562,0.01480115,0.001222924,0.002354165,0.1344398,0.007274172,0.1624857,0.00618613,0.3649999],"study_design_scores_gemma":[0.0003417557,0.001623906,0.1312913,0.0008673489,0.003040036,0.001576094,0.0006560487,0.3593219,0.008082114,0.4818583,0.01105637,0.0002849604],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06982437,0.001545212,0.9221073,0.0009476459,0.000192298,0.0002960546,0.0008113479,0.0006472159,0.003628569],"genre_scores_gemma":[0.8038766,0.000334622,0.1926751,0.0006462455,0.0002634306,0.0007049309,0.0004527678,0.0003064824,0.000739894],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1504866,"threshold_uncertainty_score":0.7958586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01360757930621634,"score_gpt":0.2589832695208897,"score_spread":0.2453756902146734,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}