{"id":"W3168375008","doi":"10.1371/journal.pone.0252748","title":"Age-structured Jolly-Seber model expands inference and improves parameter estimation from capture-recapture data","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"North Pacific Research Board; U.S. Geological Survey; U.S. Fish and Wildlife Service; Schad Foundation; Environment and Climate Change Canada; Churchill Northern Studies Centre; World Wildlife Fund Canada; University of Washington; University of Alberta; Parks Canada; World Wildlife Fund","keywords":"Mark and recapture; Population; Demography; Vital rates; Biology; Abundance (ecology); Age structure; Population model; Ursus maritimus; Wildlife; Estimation; Population size; Population growth; Geography; Statistics; Ecology; Mathematics","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":[0.003128854,0.00119014,0.001690069,0.0007590949,0.0006361221,0.001269172,0.003732681,0.002093573,0.003251887],"category_scores_gemma":[0.00712616,0.0009430346,0.001739184,0.0006644672,0.001101454,0.002166379,0.001293215,0.001903491,0.0008997225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008717627,"about_ca_system_score_gemma":0.001668221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01300141,"about_ca_topic_score_gemma":0.0152321,"domain_scores_codex":[0.9991387,0.0002793649,0.0000438213,0.0003577055,0.00008351722,0.0000968788],"domain_scores_gemma":[0.99655,0.001728109,0.0007608843,0.000380875,0.0004116272,0.0001684815],"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":[0.0001889338,0.000118461,0.02599699,0.0001277822,0.0002590257,0.0004430242,0.0002994316,0.9203579,0.002272352,0.0297027,0.003258859,0.01697456],"study_design_scores_gemma":[0.00003862945,0.00006120959,0.002904209,0.00002688742,0.0000822181,0.0001388287,0.00002627322,0.983869,0.0002089912,0.01086737,0.001747364,0.00002910169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2439847,0.001190213,0.743374,0.001326532,0.0002983727,0.0001637165,0.001849551,0.001035061,0.006777885],"genre_scores_gemma":[0.9022866,0.0009163463,0.07883162,0.0008418254,0.0002511819,0.0004791781,0.002142092,0.0003406067,0.01391063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01300141,"threshold_uncertainty_score":0.02585149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06357695883444604,"score_gpt":0.2544536322341222,"score_spread":0.1908766733996762,"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."}}