{"id":"W7133281730","doi":"","title":"Stock assessment 2023 Snow Crab","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":"Fisheries and Oceans Canada","keywords":"Stock (firearms); Population; Snow; Climate change; Ecosystem; Fish stock; Stock assessment","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0008302049,0.0003936345,0.0002078292,0.003340388,0.0005637535,0.000721626,0.0008234446,0.0002778849,0.02422738],"category_scores_gemma":[0.001596431,0.0001468229,0.0004699147,0.001733539,0.0001127444,0.000454776,0.0005067028,0.0002948081,0.008770557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002115785,"about_ca_system_score_gemma":0.003137327,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2535352,"about_ca_topic_score_gemma":0.4614074,"domain_scores_codex":[0.9995183,0.00002888495,0.00004843219,0.00003196489,0.0002971693,0.0000753725],"domain_scores_gemma":[0.998211,0.00003800564,0.0001664296,0.00005948198,0.001409686,0.0001153481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002819245,0.0001533038,0.4397401,0.0004871409,0.0001334518,0.0004574491,0.0005002937,0.005155492,0.001647555,0.00409745,0.1846562,0.3626897],"study_design_scores_gemma":[0.00003423105,0.0002412546,0.5835564,0.0004714967,0.00008396336,0.0003203801,0.001209663,0.008077128,0.00219605,0.001577271,0.4021576,0.00007458191],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.268034,0.002003647,0.009245149,0.001390293,0.0002724947,0.002123001,0.3758273,0.001061685,0.3400424],"genre_scores_gemma":[0.4213653,0.002485645,0.01451001,0.0005770834,0.00008355459,0.001121418,0.348472,0.0001468836,0.2112382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7464647,"threshold_uncertainty_score":0.504119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01156604917430448,"score_gpt":0.2681678282588098,"score_spread":0.2566017790845053,"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."}}