{"id":"W7133270613","doi":"","title":"2HJ3KLNOP4R snow crab","year":2022,"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":"Snow; Abundance (ecology); Submarine pipeline; Biomass (ecology); Fishing; Climate change","routes":{"ca_aff":false,"ca_fund":true,"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":[0.0003305704,0.0003092383,0.0001731501,0.0009511281,0.0005393765,0.0007605807,0.0004805931,0.0002240474,0.02703559],"category_scores_gemma":[0.0004034356,0.0002178728,0.0004590722,0.0007941104,0.0001814157,0.0003065004,0.00157238,0.0003198357,0.009436258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001465732,"about_ca_system_score_gemma":0.001391333,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03397235,"about_ca_topic_score_gemma":0.06323078,"domain_scores_codex":[0.9994594,0.00002915301,0.00003251206,0.00009975224,0.0002096136,0.0001695977],"domain_scores_gemma":[0.9991804,0.00002073626,0.0001923393,0.00005112217,0.0003051351,0.0002502534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001479032,0.0001894468,0.5373919,0.0004735143,0.0001630099,0.0008924527,0.0007542935,0.002146993,0.02982932,0.001156583,0.07743903,0.3480844],"study_design_scores_gemma":[0.00001565861,0.0001540577,0.9098489,0.00001884432,0.00001029879,0.0001875658,0.0003805796,0.0004886014,0.0009809399,0.00007645694,0.0878263,0.00001191732],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8510319,0.0007252209,0.001925537,0.0005628961,0.0002905045,0.0002717848,0.02605355,0.000862343,0.1182763],"genre_scores_gemma":[0.7787361,0.0004906901,0.002953714,0.000653916,0.0001690944,0.0001704834,0.03469047,0.0002107751,0.1819249],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9660277,"threshold_uncertainty_score":0.09044307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008759380252353677,"score_gpt":0.2309674801230479,"score_spread":0.2222080998706943,"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."}}