{"id":"W7133286207","doi":"","title":"Assessment of 2HJ3KLNOP4R Snow Crab in 2022","year":2023,"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":"Index (typography); Submarine pipeline; Fishing; Biomass (ecology); Snow; Climate change; Coast guard","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":[0.00251395,0.0004418521,0.0002314052,0.001355069,0.0002855294,0.001083299,0.0006302482,0.0003724067,0.001975041],"category_scores_gemma":[0.002131361,0.0002003693,0.0006819853,0.0008296235,0.0002308958,0.0005157114,0.001413985,0.0003266774,0.0006447045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002395803,"about_ca_system_score_gemma":0.002145554,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07012995,"about_ca_topic_score_gemma":0.09878365,"domain_scores_codex":[0.9987942,0.0001941523,0.00005214801,0.0001155838,0.0005831025,0.000260786],"domain_scores_gemma":[0.9983182,0.00009284046,0.000261202,0.00006544893,0.001032412,0.0002298681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003842054,0.0001038705,0.8595098,0.0002417369,0.0002904951,0.0008306968,0.0005494646,0.04134268,0.004923343,0.002609582,0.01009026,0.07912383],"study_design_scores_gemma":[0.00001858285,0.0003383233,0.8828682,0.0001035063,0.00006880834,0.0002603284,0.001934758,0.07298448,0.002720665,0.0008702615,0.0377778,0.00005439856],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9591362,0.0002946131,0.00629291,0.000564841,0.0000843445,0.0001794476,0.009909705,0.00024198,0.02329587],"genre_scores_gemma":[0.9857432,0.0001432834,0.00262758,0.0001145138,0.00001327961,0.00008509622,0.006321756,0.00003041778,0.004920888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9298701,"threshold_uncertainty_score":0.1394435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01016627081915515,"score_gpt":0.2639063749877149,"score_spread":0.2537401041685597,"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."}}