{"id":"W4403081488","doi":"10.1002/1438-390x.12198","title":"Modeling movements improves capture–recapture estimates for mobile species with sparse data: Polar bears ( <i>Ursus maritimus</i> ) in <scp>Viscount Melville</scp> sound","year":2024,"lang":"en","type":"article","venue":"Population Ecology","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Pacific Insight Electronics (Canada); Government of Northwest Territories","funders":"Environment Canada; Nunavut Wildlife Management Board; Environment and Climate Change Canada; Indigenous and Northern Affairs Canada; University of Washington; World Wildlife Fund","keywords":"Ursus maritimus; Mark and recapture; Biology; Sound (geography); Polar; Ursus; Ecology; Zoology; Oceanography; Demography; Arctic","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":[0.003001409,0.0005717319,0.0004863891,0.0005514341,0.0006283736,0.001202891,0.001368113,0.0006890725,0.001018435],"category_scores_gemma":[0.005365782,0.000490743,0.000834136,0.0004108654,0.0003616444,0.000782662,0.0007858898,0.0007350041,0.0001607822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001718799,"about_ca_system_score_gemma":0.001703777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2294904,"about_ca_topic_score_gemma":0.2989794,"domain_scores_codex":[0.9993081,0.0002637171,0.00003193877,0.0002829314,0.00003360744,0.00007969047],"domain_scores_gemma":[0.9975555,0.001524428,0.0004091158,0.0001653507,0.0002107913,0.0001347884],"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":[0.0002594074,0.0002224906,0.4355461,0.00007911089,0.0005346118,0.0001908024,0.0003103351,0.5405422,0.001834229,0.0009926832,0.001200085,0.01828796],"study_design_scores_gemma":[0.00001637687,0.00006201963,0.05450182,0.00002230806,0.00008621295,0.00003442195,0.0001339154,0.9441006,0.0002842035,0.0003028009,0.0004364897,0.00001893772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796219,0.000100137,0.01905312,0.000257307,0.00001887758,0.00002311642,0.0002022301,0.0001245755,0.0005985592],"genre_scores_gemma":[0.9937155,0.00002559135,0.005575277,0.00006083097,0.000009831197,0.00001330745,0.0002166484,0.00001975899,0.0003633305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2294904,"threshold_uncertainty_score":0.4563093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02722612011740104,"score_gpt":0.2683881017262045,"score_spread":0.2411619816088034,"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."}}