{"id":"W6958384052","doi":"10.6084/m9.figshare.24309955","title":"Additional file 1 of New approaches and technical considerations in detecting outlier measurements and trajectories in longitudinal children growth data","year":2023,"lang":"en","type":"article","venue":"Open MIND","topic":"Reliability and Agreement in Measurement","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; University of Toronto","funders":"","keywords":"Outlier; Table (database); Anomaly detection; Cluster analysis; Measure (data warehouse); Lookup table; Section (typography); Type I and type II errors","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002994026,0.00009393738,0.0002241159,0.0001781475,0.0000956806,0.0002493614,0.0004484593,0.00005256725,0.1078224],"category_scores_gemma":[0.01140674,0.00007632736,0.00001515335,0.0005075725,0.0001242483,0.0004986055,0.0006091542,0.0001239951,0.00004057103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002231237,"about_ca_system_score_gemma":0.0001649756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001335726,"about_ca_topic_score_gemma":0.003683732,"domain_scores_codex":[0.9979187,0.0001396504,0.0005249062,0.00054709,0.0007190568,0.0001505676],"domain_scores_gemma":[0.997144,0.00226794,0.000120395,0.0003526941,0.00005499606,0.00005990959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002658243,0.0001471431,0.5030606,0.000005382748,0.00002220299,0.000004313948,0.0005567438,0.00006416797,0.00008658508,0.00002197115,0.4666898,0.02931456],"study_design_scores_gemma":[0.0004346962,0.00004105971,0.9917748,0.0001093499,0.000006469429,0.00000943524,0.0005028766,0.0002205413,0.0001512382,0.004663342,0.001975245,0.000110942],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9004455,0.00009576239,0.0000709735,0.001679823,0.00007557184,0.001635694,0.09083053,0.00000647415,0.005159659],"genre_scores_gemma":[0.9799872,0.000003281043,0.01687553,0.00001208566,0.00003207532,0.00006338281,0.002764748,0.000005270425,0.0002564466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4887142,"threshold_uncertainty_score":0.9969206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.554596492596195,"score_gpt":0.4010752672679385,"score_spread":0.1535212253282566,"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."}}