{"id":"W2060851763","doi":"10.1139/f04-245","title":"Practical application of meta-analysis results: avoiding the double use of data","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Meta-analysis; Computer science; Stock assessment; Stock (firearms); Data type; Data mining; Data set; Econometrics; Statistics; Mathematics; Artificial intelligence; Fishery; Biology; Engineering; Fishing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5114819,0.007281463,0.01582859,0.01122844,0.002207973,0.009434841,0.009431245,0.007931557,0.006122848],"category_scores_gemma":[0.8018957,0.004385915,0.01550391,0.01603055,0.006013714,0.0127066,0.008825985,0.01418766,0.001351727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004225855,"about_ca_system_score_gemma":0.007144581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004774687,"about_ca_topic_score_gemma":0.005788857,"domain_scores_codex":[0.2903189,0.6462802,0.02567443,0.01508352,0.02189446,0.0007485531],"domain_scores_gemma":[0.169256,0.7392191,0.0158918,0.06184934,0.0125296,0.001254032],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.005284561,0.0002822915,0.01807563,0.03832762,0.1521239,0.004318034,0.004794589,0.03643921,0.003302657,0.09141958,0.06446099,0.581171],"study_design_scores_gemma":[0.005401013,0.001734694,0.01038906,0.01591898,0.05361281,0.003777584,0.001290461,0.1171672,0.006621872,0.6929948,0.08949772,0.001593838],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004362022,0.01180595,0.9544303,0.01767529,0.003957622,0.002214948,0.00104025,0.002005632,0.00250792],"genre_scores_gemma":[0.08535803,0.003090708,0.8978984,0.005977432,0.001605648,0.004003142,0.0002933529,0.0006759582,0.001097265],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4885181,"threshold_uncertainty_score":0.6024297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1839912797061652,"score_gpt":0.3143436790293228,"score_spread":0.1303523993231576,"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."}}