{"id":"W2123069546","doi":"10.1111/j.1467-2979.2011.00435.x","title":"Examining the knowledge base and status of commercially exploited marine species with the RAM Legacy Stock Assessment Database","year":2011,"lang":"en","type":"article","venue":"Fish and Fisheries","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":405,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Dalhousie University","funders":"New Jersey Sea Grant Consortium","keywords":"Stock assessment; Maximum sustainable yield; Stock (firearms); Fishing; Fishery; Database; Fish stock; Population; Fisheries science; Fisheries management; Marine ecosystem; Sustainable yield; Biodiversity; Geography; Ecosystem; Ecology; 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":[0.008012862,0.0004144413,0.001021849,0.02054411,0.0003074554,0.001609019,0.00144445,0.0005399573,0.001921211],"category_scores_gemma":[0.02242003,0.0003127726,0.001390634,0.01131359,0.0002229355,0.002018542,0.001483496,0.0004182391,0.000534418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005180783,"about_ca_system_score_gemma":0.001068817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01170603,"about_ca_topic_score_gemma":0.01666667,"domain_scores_codex":[0.9962974,0.0007966133,0.0009997876,0.001075229,0.000685266,0.0001456786],"domain_scores_gemma":[0.9751853,0.01393305,0.004754636,0.002963399,0.002715496,0.0004481533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003157977,0.00009581183,0.8103438,0.006218005,0.009080441,0.0002917159,0.0004449057,0.004564313,0.001444665,0.00121507,0.01495639,0.1510291],"study_design_scores_gemma":[0.0001604628,0.0002433994,0.8879324,0.005408924,0.01205314,0.0006791615,0.001218385,0.01968163,0.00319323,0.002383778,0.06689424,0.000151262],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.499121,0.01841638,0.009760026,0.0008191009,0.00006534918,0.0003119259,0.4633853,0.0004694993,0.007651334],"genre_scores_gemma":[0.6822034,0.00518496,0.02314833,0.0004113235,0.00004907867,0.0006056359,0.2878208,0.00005998244,0.0005165986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02054411,"threshold_uncertainty_score":0.04237658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07648938765606933,"score_gpt":0.259878326536583,"score_spread":0.1833889388805137,"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."}}