{"id":"W2021998150","doi":"10.1111/faf.12061","title":"Giants' shoulders 15 years later: lessons, challenges and guidelines in fisheries meta‐analysis","year":2013,"lang":"en","type":"article","venue":"Fish and Fisheries","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of British Columbia; Conservation International; Pew Charitable Trusts","keywords":"Fisheries management; Stock assessment; Population; Selection bias; Meta-analysis; Fishery; Selection (genetic algorithm); Fisheries science; Geography; Fishing; Computer science; Biology; Sociology; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002779852,0.0002332632,0.0005495636,0.0001088696,0.0001020272,0.0002199796,0.0002107198,0.0001370311,0.01239553],"category_scores_gemma":[0.00008883528,0.0001983822,0.0001312523,0.0003398845,0.0005104046,0.0008007356,0.0004563025,0.0001877159,0.00002498488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001899218,"about_ca_system_score_gemma":0.000007043919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004983305,"about_ca_topic_score_gemma":0.01385295,"domain_scores_codex":[0.998347,0.0000840818,0.0003274664,0.0005118048,0.0002940455,0.0004355322],"domain_scores_gemma":[0.9993716,0.00007634937,0.00005621215,0.0002997925,0.00002376507,0.0001722871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004434181,0.0000696609,0.6327924,0.00007999985,0.004285355,0.00004505043,0.002858424,0.000009569709,0.0000460772,0.00008356627,0.07366754,0.286018],"study_design_scores_gemma":[0.0002531093,0.00007910651,0.6767613,0.000002899701,0.001296277,0.000006955616,0.001564487,0.000330528,0.00001788227,0.0009458989,0.3183765,0.0003651404],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6508171,0.005583893,0.00002708307,0.125881,0.0001299336,0.0008963146,0.0001081396,0.0001709122,0.2163856],"genre_scores_gemma":[0.9078613,0.05654887,0.003686675,0.005826166,0.0001292178,0.0005340036,0.00009563552,0.00009127083,0.02522686],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2856528,"threshold_uncertainty_score":0.9885073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08517162994805322,"score_gpt":0.2922462426771342,"score_spread":0.207074612729081,"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."}}