{"id":"W4289667425","doi":"10.1139/cjfas-2021-0262","title":"Estimating survival probabilities of Cambridge Bay Arctic char using acoustic telemetry data and Bayesian multistate capture–recapture models","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Fisheries and Oceans Canada; University of Windsor; University of Manitoba","funders":"Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada; Polar Knowledge Canada","keywords":"Arctic char; Bay; Salvelinus; Estuary; Telemetry; Fishery; Habitat; Environmental science; Mark and recapture; Threatened species; Arctic; Ecology; Geography; Biology; Fish <Actinopterygii>; Trout; Computer science; Population","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.003809355,0.0007407092,0.0005570527,0.001316237,0.0004693208,0.0008111495,0.0009649493,0.0005530008,0.00103025],"category_scores_gemma":[0.006846264,0.000573665,0.0007843546,0.0005259845,0.0003055647,0.0005989048,0.0007506376,0.0005187221,0.0002426491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001811851,"about_ca_system_score_gemma":0.001391709,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1918516,"about_ca_topic_score_gemma":0.2509071,"domain_scores_codex":[0.9993587,0.000271772,0.00003901139,0.0001815871,0.00005914535,0.00008982958],"domain_scores_gemma":[0.9968959,0.001663838,0.0006572181,0.0001870128,0.0003793489,0.0002166814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002630736,0.00008895544,0.7039635,0.00006343847,0.0004616535,0.0001713175,0.0002852658,0.269273,0.001147337,0.0008778788,0.0009354043,0.02246915],"study_design_scores_gemma":[0.00001611773,0.000085678,0.1688831,0.0000428181,0.00013303,0.00007592358,0.0002354562,0.8288983,0.0002821797,0.0009057642,0.0004007175,0.00004097817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827338,0.0002241837,0.01603606,0.0001075942,0.00001294882,0.00001652167,0.0004207652,0.00006080606,0.0003873332],"genre_scores_gemma":[0.9940886,0.0001172805,0.004074018,0.00001627977,0.000009704424,0.00001902023,0.001082644,0.000008686406,0.0005837193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8081484,"threshold_uncertainty_score":0.3814697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03293237577510481,"score_gpt":0.2334938884591499,"score_spread":0.200561512684045,"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."}}