{"id":"W2528896810","doi":"10.1002/jwmg.21178","title":"Comparing resource selection and demographic models for predicting animal density","year":2016,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; Alberta Environment and Protected Areas; University of Alberta; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Carrying capacity; Selection (genetic algorithm); Habitat; Population; Wildlife; Covariate; Population density; Ecology; Statistics; Land cover; Density dependence; Estimator; Geography; Land use; Mathematics; Computer science; Biology; Demography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.007770298,0.0006967404,0.000527204,0.001289094,0.0003613718,0.0008001842,0.001068161,0.000556764,0.0008785952],"category_scores_gemma":[0.01544104,0.0003690682,0.0008584592,0.0006354153,0.0005434974,0.0009424938,0.000694063,0.0005604159,0.000190468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001946093,"about_ca_system_score_gemma":0.001106696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06790969,"about_ca_topic_score_gemma":0.02877732,"domain_scores_codex":[0.9985803,0.0009595493,0.00005510078,0.00023988,0.00008604884,0.00007905831],"domain_scores_gemma":[0.9806668,0.0167553,0.0008088286,0.0005785334,0.000852815,0.0003376547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002190871,0.00007751909,0.1457683,0.00002275491,0.000225157,0.00003121484,0.00006756887,0.8366919,0.0002129427,0.001232454,0.0004218846,0.0150291],"study_design_scores_gemma":[0.000007716403,0.00002576419,0.007904114,0.000002649278,0.00001060774,0.000006568429,0.00001107262,0.991247,0.00003955562,0.0006996062,0.00003945371,0.000005986038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9325407,0.000160208,0.06527759,0.0003465125,0.00001864302,0.00005592381,0.0004181503,0.000199282,0.0009829368],"genre_scores_gemma":[0.9920323,0.00004182213,0.007160302,0.00002960493,0.00001649788,0.00003985588,0.0003881041,0.00001349774,0.0002780143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06790969,"threshold_uncertainty_score":0.1350288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01769019626449758,"score_gpt":0.2142117697801363,"score_spread":0.1965215735156387,"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."}}