{"id":"W2722311436","doi":"10.1002/jwmg.21277","title":"Annual survival and seasonal hunting mortality of midcontinent snow geese","year":2017,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Avian ecology and behavior","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Université de Montréal","funders":"Environment and Climate Change Canada; U.S. Fish and Wildlife Service; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; California Department of Fish and Game","keywords":"Subarctic climate; Vital rates; Arctic; Waterfowl; Geography; Population; Snow; Wildlife; Habitat; Ecology; Bay; Population growth; Hunting season; Mortality rate; Seasonality; Mark and recapture; 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":[0.0006457893,0.0001776164,0.0001447879,0.0004476174,0.0001947777,0.0003321606,0.0002393359,0.0001816157,0.0006189399],"category_scores_gemma":[0.0008731472,0.00008959139,0.0002707354,0.0002202085,0.0001507361,0.0002466704,0.0002306855,0.0001469448,0.0001176825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000394104,"about_ca_system_score_gemma":0.0002992746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04555247,"about_ca_topic_score_gemma":0.1024792,"domain_scores_codex":[0.9999055,0.00002032785,0.000007885065,0.00002428111,0.00001839376,0.00002365959],"domain_scores_gemma":[0.9993415,0.0001411498,0.0001854038,0.00004894997,0.0001490429,0.0001339371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003682782,0.00001876781,0.9969264,0.000004166821,0.00004118019,0.00002790076,0.00009368184,0.0008213602,0.0006229274,0.00001766802,0.00005260576,0.001336499],"study_design_scores_gemma":[8.692992e-7,0.0000259442,0.9983015,0.000001838719,0.00000638001,0.00001684662,0.00005939701,0.001434604,0.00006832611,0.00001140181,0.00007099664,0.000001814194],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996179,0.0000219841,0.0001106044,0.000003835118,9.913931e-7,0.000001297163,0.0001609374,0.000002567246,0.00007981406],"genre_scores_gemma":[0.9993394,0.00001602147,0.0001275116,0.000005165608,0.000001365937,0.000002941678,0.0003853545,0.000001301639,0.0001210552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04555247,"threshold_uncertainty_score":0.09057462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01884449249040623,"score_gpt":0.2755653455201647,"score_spread":0.2567208530297585,"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."}}