{"id":"W4400459525","doi":"10.1111/2041-210x.14377","title":"Robust point and variance estimation for meta‐analyses with selective reporting and dependent effect sizes","year":2024,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Australian Research Council; National Natural Science Foundation of China","keywords":"Estimator; Point estimation; Weighting; Econometrics; Meta-analysis; Statistics; Variance (accounting); Inference; Computer science; Statistical inference; Publication bias; Benchmark (surveying); Standard error; Mathematics; Artificial intelligence; Confidence interval","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1442159,0.0001753856,0.001912323,0.0003284207,0.0001645918,0.0002583862,0.00009321004,0.0001144852,0.00007289163],"category_scores_gemma":[0.0579448,0.0000822197,0.000215241,0.0005673959,0.00008282979,0.000275268,0.00004750511,0.0001384219,0.000003507612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004482107,"about_ca_system_score_gemma":0.00003832964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002717139,"about_ca_topic_score_gemma":0.0002763688,"domain_scores_codex":[0.9852176,0.01022411,0.003132883,0.0008502842,0.0003984826,0.0001765884],"domain_scores_gemma":[0.9786711,0.01871707,0.001943492,0.0003891054,0.0002263196,0.00005287934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000365093,0.0001152283,0.5902693,0.002132969,0.01167734,0.00006730704,0.00340051,0.0389678,0.003728628,0.019538,0.001724385,0.3280135],"study_design_scores_gemma":[0.0002584974,0.0003054028,0.2375444,0.0000465547,0.004341016,0.0002054819,0.0002564115,0.6841615,0.0003211469,0.07225905,0.0001481818,0.0001522857],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1385622,0.006865531,0.8533831,0.0002749375,0.0001164054,0.0006201343,0.00000220828,0.000006793472,0.0001686748],"genre_scores_gemma":[0.5658521,0.00002741868,0.4336347,0.00001894853,0.000014408,0.0002047346,0.000001134327,0.000004205031,0.0002423685],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6451938,"threshold_uncertainty_score":0.9499905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6728186004709271,"score_gpt":0.5936550125817566,"score_spread":0.07916358788917055,"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."}}