{"id":"W6921001302","doi":"10.6084/m9.figshare.14190231","title":"Additional file 1 of Waist circumference prediction for epidemiological research using gradient boosted trees","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Waist; Circumference; Table (database); Decision tree; Pattern recognition (psychology)","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":["insufficient_payload"],"category_scores_codex":[0.0002585934,0.0001027348,0.0001605694,0.0000342248,0.0002467648,0.00001270997,0.0001455467,0.0001019945,0.9916594],"category_scores_gemma":[0.01732588,0.00009978373,0.00007521283,0.000252763,0.00007778594,0.0001342487,0.0002340295,0.0002190156,0.00144816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002985637,"about_ca_system_score_gemma":0.00007243417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001587334,"about_ca_topic_score_gemma":0.00003860312,"domain_scores_codex":[0.9981611,0.0002949174,0.0002697021,0.0005013701,0.0003835904,0.0003893234],"domain_scores_gemma":[0.9948224,0.00465609,0.0001085276,0.0002258618,0.00005539477,0.0001317465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005741333,0.00006061418,0.0002036623,0.00003185804,0.000004766568,0.000007140488,0.00003126442,0.0001685755,0.0006567065,0.000002415426,0.9952773,0.003549996],"study_design_scores_gemma":[0.0001508045,0.0001025094,0.1792096,0.001075327,0.000003857744,0.00001586024,0.0001127858,0.009346977,0.0007720212,0.0004034967,0.8086796,0.0001270931],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001592685,0.00002032545,0.00003188684,0.00006484,0.0000142961,0.0003364003,0.9937869,0.00002045829,0.004132259],"genre_scores_gemma":[0.0334865,0.000003255869,0.002760557,0.0001758549,0.00009025668,0.001230323,0.9617428,0.00001515867,0.0004952935],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9902112,"threshold_uncertainty_score":0.9993293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2643214070810933,"score_gpt":0.3690682163823916,"score_spread":0.1047468093012983,"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."}}