{"id":"W2133461511","doi":"","title":"Fisheries in large marine ecosystems: Descriptions and diagnoses","year":2008,"lang":"en","type":"article","venue":"UEA Digital Repository (University of East Anglia)","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Fishing; Fishery; Trophic level; Stock (firearms); Stock assessment; Marine fisheries; Marine ecosystem; Index (typography); Ecosystem; Fisheries management; Geography; Environmental science; Ecology; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003766844,0.0000800132,0.0001326307,0.00004393355,0.0002006212,0.00003070437,0.0001543612,0.00004623947,0.0008012813],"category_scores_gemma":[0.00002643813,0.00009524382,0.00004315082,0.0001842685,0.0003291129,0.0008634588,0.0005258321,0.00008118508,0.00003114021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006329863,"about_ca_system_score_gemma":0.00001265395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002490262,"about_ca_topic_score_gemma":0.003151611,"domain_scores_codex":[0.9993079,0.00002068893,0.00009236934,0.0001969452,0.0001951349,0.0001869645],"domain_scores_gemma":[0.9996651,0.00002533649,0.00004346738,0.0001553737,0.00001366896,0.00009709848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003102839,0.0001098398,0.9939936,0.00001332712,0.000006699353,0.0002989368,0.0006279799,0.000001881446,0.00009116207,0.00002861333,0.001984179,0.00281279],"study_design_scores_gemma":[0.0004241689,0.0001044426,0.9261721,0.000009893235,0.000004299507,0.0000927458,0.003276359,0.0002556286,0.00002619274,0.00005011908,0.06944039,0.0001436933],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7296122,0.00001192839,0.00002039815,0.00005360125,0.00002803593,0.00007241304,0.00001506214,0.00001605941,0.2701703],"genre_scores_gemma":[0.9816759,0.00005055226,0.00009146035,0.000007208128,0.00001227092,5.322913e-7,0.00001058065,0.000005405885,0.01814611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2520637,"threshold_uncertainty_score":0.8773468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01367861285510919,"score_gpt":0.1711279865703487,"score_spread":0.1574493737152395,"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."}}