{"id":"W6920734896","doi":"10.6084/m9.figshare.13294966","title":"Additional file 1 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":"Sample (material); Decoding methods; File format; Data collection; Data file","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":[0.00667254,0.001608838,0.002087914,0.01039292,0.001050314,0.002251493,0.001872948,0.001434052,0.8805031],"category_scores_gemma":[0.1047514,0.001199489,0.001769339,0.01129303,0.0003836947,0.002866022,0.001981628,0.001016404,0.15685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002506002,"about_ca_system_score_gemma":0.005255692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006394589,"about_ca_topic_score_gemma":0.01503163,"domain_scores_codex":[0.9961909,0.0009519493,0.001543314,0.0004672653,0.0005793048,0.0002672596],"domain_scores_gemma":[0.8225173,0.1439804,0.01050071,0.004214857,0.01775209,0.001034586],"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":[0.001043814,0.0001163694,0.001845471,0.04771431,0.0001638434,0.0000882917,0.0003240621,0.0004254824,0.0002720087,0.001182979,0.9260538,0.02076975],"study_design_scores_gemma":[0.01211302,0.0005259322,0.02505559,0.02955759,0.001078181,0.000457829,0.001068959,0.001676207,0.001823287,0.01013232,0.9162206,0.0002904251],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0001861608,0.0000546214,0.0003493768,0.0001134113,0.00002335018,0.0006141938,0.9974529,0.0002695209,0.0009365229],"genre_scores_gemma":[0.008720311,0.0006589976,0.0194172,0.0009165013,0.0002098361,0.03933159,0.9095899,0.001423162,0.01973242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9933274,"threshold_uncertainty_score":0.1704478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.772227508548318,"score_gpt":0.4698714823117126,"score_spread":0.3023560262366054,"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."}}