{"id":"W4296762334","doi":"10.31222/osf.io/9we43","title":"Reducing the residue of retractions in evidence synthesis: Ways to minimize inappropriate citation and use of retracted data","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Academic integrity and plagiarism","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Alfred P. Sloan Foundation","keywords":"Citation; Systematic review; Medical literature; MEDLINE; Evidence-based medicine; Computer science; Data science; Psychology; Medicine; Political science; Library science; Law","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.008972097,0.0001245695,0.0003172521,0.0002329182,0.0002669669,0.00006637063,0.001060382,0.0008022243,0.0003382382],"category_scores_gemma":[0.04315015,0.0001031494,0.00004029346,0.000595011,0.0002451609,0.0005256702,0.001046305,0.002883386,0.000001223574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001300052,"about_ca_system_score_gemma":0.0006048046,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1186843,"about_ca_topic_score_gemma":0.008108946,"domain_scores_codex":[0.9969149,0.001091724,0.0006319426,0.000483712,0.0006863892,0.0001913496],"domain_scores_gemma":[0.9914005,0.00704329,0.0004949914,0.0007947012,0.0002089796,0.00005757067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.002615665,0.0006860111,0.04003222,0.00209702,0.0006249624,0.00003211007,0.6033602,0.007182035,0.01149929,0.1883396,0.08976488,0.05376603],"study_design_scores_gemma":[0.0006517952,0.0002469614,0.5360218,0.01488,0.00122878,0.00001153564,0.3209724,0.01226881,0.009549164,0.06992623,0.03194428,0.002298239],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9426847,0.0008177468,0.00660841,0.03348794,0.00133781,0.002351794,0.0007729579,0.00008378029,0.01185486],"genre_scores_gemma":[0.9798866,0.00301799,0.01628824,0.0001219849,0.000111776,0.00006390469,0.00003848465,0.0000111547,0.0004598987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4959896,"threshold_uncertainty_score":0.999417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2284432204022186,"score_gpt":0.375603707732273,"score_spread":0.1471604873300544,"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."}}