{"id":"W6958379795","doi":"10.6084/m9.figshare.13290662.v1","title":"Additional file 1 of Developing a coding taxonomy to analyze dental regulatory complaints","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Reliability and Agreement in Measurement","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Toronto","funders":"","keywords":"Coding (social sciences); Reliability (semiconductor); Taxonomy (biology); Table (database); Data collection","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004656473,0.000860751,0.0008353295,0.004070149,0.001144157,0.001240411,0.001392566,0.0009763328,0.8410768],"category_scores_gemma":[0.07112961,0.0005173316,0.0006736086,0.005028178,0.0003259208,0.00176005,0.001236564,0.001099281,0.138603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001836961,"about_ca_system_score_gemma":0.003444884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006792917,"about_ca_topic_score_gemma":0.01425475,"domain_scores_codex":[0.9974988,0.000738555,0.0005882431,0.0002813484,0.0006825438,0.0002105172],"domain_scores_gemma":[0.9102448,0.06827919,0.005360619,0.003031459,0.01225093,0.0008330822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001512309,0.0001227549,0.002009075,0.0008272814,0.00001016559,0.00002621749,0.000165937,0.0001640804,0.00004798742,0.0007397553,0.9831144,0.01262114],"study_design_scores_gemma":[0.002867525,0.0004406107,0.05547198,0.004373584,0.0001074214,0.0004538179,0.002697302,0.002807471,0.00125187,0.01519994,0.9141145,0.0002140183],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.0008312776,0.00001605516,0.001396869,0.0002934576,0.00005499358,0.001514611,0.9910361,0.0003451368,0.004511471],"genre_scores_gemma":[0.0223099,0.000219799,0.02078743,0.001008381,0.0002431506,0.03413022,0.8884115,0.001274913,0.03161471],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8410768,"threshold_uncertainty_score":0.2266847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3230252684778103,"score_gpt":0.3359351863011782,"score_spread":0.01290991782336792,"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."}}