{"id":"W7133284050","doi":"","title":"Science Response : updated LURS abundance and genetic analyses","year":2024,"lang":"en","type":"other","venue":"Federal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Endangered species; Threatened species; Population; Abundance (ecology); Critically endangered; Population size; Smelt; Quadrat; Wildlife","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002662282,0.0006372653,0.0006126192,0.003728362,0.0008561045,0.001524651,0.001337218,0.001866682,0.1028349],"category_scores_gemma":[0.009700824,0.0004155292,0.0004437315,0.002339493,0.000346366,0.001175912,0.001713097,0.002415456,0.04590287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00130629,"about_ca_system_score_gemma":0.002466086,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01856409,"about_ca_topic_score_gemma":0.04732721,"domain_scores_codex":[0.9982764,0.0002101665,0.0001680481,0.0001897987,0.0009746365,0.0001810301],"domain_scores_gemma":[0.988761,0.0009108114,0.0005502803,0.0008706487,0.008095738,0.00081141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00005868166,0.00002292543,0.002516511,0.00009818683,0.00001172,0.00006375012,0.00003184503,0.000039962,0.0002218955,0.0001227803,0.9804771,0.01633475],"study_design_scores_gemma":[0.00004681992,0.00002718416,0.01704655,0.0001029471,0.00001127796,0.00005849784,0.0001324578,0.0001151562,0.0001963682,0.0003092143,0.9819351,0.00001827284],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01463062,0.002207392,0.003728771,0.1140514,0.05522149,0.001319668,0.5744246,0.008487605,0.2259285],"genre_scores_gemma":[0.04175504,0.002701953,0.0121726,0.06870815,0.01754978,0.001532239,0.4311249,0.001798789,0.4226566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9814359,"threshold_uncertainty_score":0.344017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01413646766853872,"score_gpt":0.2794683930330063,"score_spread":0.2653319253644676,"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."}}