{"id":"W6977105027","doi":"10.6084/m9.figshare.14190234","title":"Additional file 2 of Waist circumference prediction for epidemiological research using gradient boosted trees","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Circumference; Decision tree; Waist; Tree (set theory); Variable (mathematics)","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":[0.00256658,0.001193832,0.001395113,0.00167177,0.0006017671,0.001809132,0.002350767,0.001565975,0.8481684],"category_scores_gemma":[0.0408236,0.0007917318,0.001186039,0.002548275,0.0002579415,0.001360061,0.001010078,0.001213637,0.1888771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008379858,"about_ca_system_score_gemma":0.001766571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005154317,"about_ca_topic_score_gemma":0.01071976,"domain_scores_codex":[0.9990939,0.0002677265,0.0001173833,0.0002730343,0.0001605501,0.00008741602],"domain_scores_gemma":[0.973118,0.02319267,0.0006728307,0.001074481,0.001580062,0.0003619197],"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":[0.0003577633,0.00009938281,0.002488698,0.003054847,0.00010374,0.00007963077,0.00005297859,0.001365104,0.000107328,0.001232669,0.9729288,0.01812899],"study_design_scores_gemma":[0.008974745,0.0005048705,0.02368887,0.004231669,0.0006718008,0.000808117,0.0003532481,0.01808253,0.001833339,0.04102774,0.8996075,0.0002154854],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0002437362,0.00003911303,0.001912229,0.0001469369,0.00003675048,0.00009200487,0.9957421,0.001098953,0.0006879851],"genre_scores_gemma":[0.01439609,0.0002631297,0.02228651,0.0008731472,0.0001726697,0.003226053,0.9448715,0.003562452,0.01034833],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8481684,"threshold_uncertainty_score":0.2165693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6877278544306521,"score_gpt":0.5092984696801275,"score_spread":0.1784293847505246,"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."}}