{"id":"W2773845804","doi":"10.1002/jwmg.21404","title":"Integrated population models facilitate ecological understanding and improved management decisions","year":2017,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Avian ecology and behavior","field":"Environmental Science","cited_by":106,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Environment and Climate Change Canada","keywords":"Aythya; Anas; Waterfowl; Population; Ecology; Anatidae; Population model; Fecundity; Wildlife; Habitat; Population ecology; Population size; Geography; Environmental resource management; Fishery; Biology; Environmental science; Demography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005668835,0.0001275548,0.000188521,0.00008336289,0.0005112053,0.000103434,0.0003527024,0.00006317076,0.000250175],"category_scores_gemma":[0.00002577851,0.00009607532,0.00007086179,0.00005011619,0.0001563413,0.0005408998,0.0004620505,0.0001584592,0.00002491448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003616673,"about_ca_system_score_gemma":0.00000213376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001728919,"about_ca_topic_score_gemma":0.00008749393,"domain_scores_codex":[0.9989333,0.00004539685,0.0003733605,0.0001948241,0.0002415831,0.0002115511],"domain_scores_gemma":[0.9992269,0.00003201892,0.0003687382,0.0002480549,0.000008614616,0.0001156648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004608058,0.000918936,0.805082,0.00003867927,0.0004507136,0.001041887,0.0005403582,0.007825221,0.0001110111,0.008935289,0.02488999,0.1497051],"study_design_scores_gemma":[0.0008909634,0.0001657553,0.9738975,0.00004035337,0.0001345313,0.00002155231,0.0007800864,0.001912455,0.000001716517,0.02029064,0.001731914,0.0001325149],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9536104,0.000011775,0.03518144,0.001560914,0.0003278113,0.0003376518,0.000003984776,0.00001397745,0.00895205],"genre_scores_gemma":[0.9900697,0.0001941345,0.008062296,0.0003479374,0.00001650301,0.00000506471,0.000001987572,0.000006704332,0.001295672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1688155,"threshold_uncertainty_score":0.393183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08500381078756587,"score_gpt":0.2787958759323972,"score_spread":0.1937920651448313,"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."}}