{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.4713273,0.003432064,0.00928817,0.009248231,0.002797373,0.01477862,0.01057931,0.01463009,0.004286861],"category_scores_gemma":[0.6776044,0.002513489,0.0107095,0.009215346,0.01521278,0.02224088,0.008075559,0.03061151,0.001903929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006701117,"about_ca_system_score_gemma":0.01351358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009665752,"about_ca_topic_score_gemma":0.0130925,"domain_scores_codex":[0.6523317,0.2817869,0.03851756,0.01175085,0.01455274,0.001060299],"domain_scores_gemma":[0.2535399,0.6814877,0.009915997,0.02343561,0.02886277,0.002757984],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005827288,0.00009127154,0.002947379,0.032282,0.01076801,0.001011975,0.006637338,0.004998836,0.0005582066,0.2177461,0.4312165,0.2911597],"study_design_scores_gemma":[0.0003547611,0.0001620637,0.001381163,0.06168969,0.003735658,0.0006686149,0.001378134,0.005552833,0.000468193,0.640632,0.2835665,0.0004103903],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.0008068113,0.3221825,0.08650211,0.5521288,0.0353523,0.0002872826,0.0005674835,0.0005827872,0.001589974],"genre_scores_gemma":[0.04063696,0.1799903,0.3288287,0.389228,0.05294372,0.002762684,0.0004878757,0.001328213,0.003793493],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5286727,"threshold_uncertainty_score":0.6519476,"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."}}