{"id":"W2101607638","doi":"10.1093/icesjms/fsp275","title":"Extracting groundfish survey indices from the Ocean Biogeographic Information System (OBIS): an example from Fisheries and Oceans Canada","year":2009,"lang":"en","type":"article","venue":"ICES Journal of Marine Science","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada; Ocean Tracking Network; Dalhousie University","funders":"Northeast Fisheries Science Center","keywords":"Groundfish; Fishery; Stock assessment; Stock (firearms); Context (archaeology); Geography; Fish stock; Marine fisheries; Oceanography; Environmental science; Fish <Actinopterygii>; Fishing; Fisheries management; Geology; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.00159245,0.000527507,0.0003374236,0.003903494,0.001256572,0.001230608,0.0008200063,0.000413104,0.001282643],"category_scores_gemma":[0.005650631,0.0001641239,0.0005378679,0.01340608,0.000409232,0.0006074162,0.0008602156,0.0006499623,0.0004255753],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00569278,"about_ca_system_score_gemma":0.01114789,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9426796,"about_ca_topic_score_gemma":0.9550751,"domain_scores_codex":[0.9982926,0.0002009806,0.0001144308,0.0001258106,0.001132956,0.0001331131],"domain_scores_gemma":[0.9941187,0.001376969,0.0001967868,0.000349322,0.003726914,0.0002312762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003505424,0.0002429751,0.4845749,0.0009898668,0.0004217173,0.00232151,0.002476245,0.01787439,0.007374223,0.004669321,0.0758123,0.4028921],"study_design_scores_gemma":[0.0001316135,0.0001134115,0.7338824,0.0004493966,0.000257977,0.0005690384,0.006973953,0.08710092,0.01131263,0.003354368,0.1556741,0.0001801714],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8072337,0.004970462,0.03716916,0.006032115,0.0002485184,0.0009641375,0.1079341,0.002261719,0.03318597],"genre_scores_gemma":[0.643593,0.004799595,0.2347152,0.0005756173,0.00009531972,0.000284498,0.1059154,0.0004425688,0.009578907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9943072,"threshold_uncertainty_score":0.1153158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02029754484775049,"score_gpt":0.2208075886307454,"score_spread":0.2005100437829949,"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."}}