{"id":"W6901854748","doi":"10.6084/m9.figshare.13294969.v1","title":"Additional file 2 of Decoding semi-automated title-abstract screening: findings from a convenience sample of reviews","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Decoding methods; Sample (material); Table (database); File format","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006663368,0.001878158,0.002719679,0.0128875,0.0009703241,0.00276603,0.002096472,0.001543093,0.8886616],"category_scores_gemma":[0.1102486,0.001365777,0.002407042,0.0142297,0.0003984267,0.003455392,0.002173124,0.000988675,0.1503926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002695597,"about_ca_system_score_gemma":0.005651855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007170809,"about_ca_topic_score_gemma":0.01645097,"domain_scores_codex":[0.99581,0.0009745308,0.00179228,0.0004946591,0.0006218819,0.0003066465],"domain_scores_gemma":[0.8151418,0.149617,0.0119504,0.003893954,0.01827672,0.001120157],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001010882,0.000105711,0.001807131,0.09397687,0.0002599445,0.0001115429,0.0003234633,0.0004380776,0.0002653876,0.001564291,0.8811058,0.01903086],"study_design_scores_gemma":[0.01284968,0.0004907213,0.02401488,0.0426617,0.001393227,0.000461392,0.0009469077,0.001495991,0.001427148,0.009872144,0.9040897,0.0002964749],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0001568892,0.00007333093,0.0002732871,0.0001224659,0.00002724147,0.0005364363,0.9976441,0.0002548623,0.000911261],"genre_scores_gemma":[0.007906031,0.0008381752,0.01540863,0.001003533,0.0002350244,0.0339226,0.9176984,0.001434977,0.02155254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9933366,"threshold_uncertainty_score":0.1588108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7653601690849835,"score_gpt":0.4690938208194289,"score_spread":0.2962663482655546,"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."}}