{"id":"W6964360932","doi":"10.25384/sage.c.5134309.v1","title":"Pattern discovery of health curves using an ordered probit model with Bayesian smoothing and functional principal component analysis","year":2020,"lang":"en","type":"other","venue":"Sage Journals Data","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Categorical variable; Bayesian probability; Markov chain Monte Carlo; Principal component analysis; Component (thermodynamics); Probit model; Health care; Process (computing); Markov chain","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009652966,0.001007367,0.001497634,0.003521514,0.0009227312,0.002491171,0.002268974,0.001876322,0.004546565],"category_scores_gemma":[0.04415159,0.0009416016,0.002361066,0.003165373,0.001376042,0.002085428,0.002043446,0.003361716,0.001124997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001820591,"about_ca_system_score_gemma":0.002829701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04075296,"about_ca_topic_score_gemma":0.03330119,"domain_scores_codex":[0.9952849,0.002997432,0.0002488875,0.0007321191,0.0005034896,0.0002331593],"domain_scores_gemma":[0.9733401,0.02198191,0.001478618,0.001666633,0.001209803,0.0003228768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003524577,0.0003140437,0.04307523,0.0003979027,0.0004432358,0.0006692122,0.00126966,0.6386877,0.001098744,0.1426549,0.007063949,0.1639729],"study_design_scores_gemma":[0.00001745278,0.00002362749,0.002558388,0.00003565016,0.00002076408,0.00005081039,0.00006234723,0.9487926,0.0001824776,0.04701468,0.001209888,0.00003132792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0315807,0.0002014307,0.964253,0.0008632921,0.00002761551,0.0001961073,0.0009258967,0.0007039809,0.001247917],"genre_scores_gemma":[0.5053524,0.0005050914,0.4848082,0.0002745912,0.0001033958,0.0007400632,0.00336376,0.0002906233,0.004561933],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04075296,"threshold_uncertainty_score":0.0810315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1224318148571157,"score_gpt":0.3375248391455166,"score_spread":0.2150930242884009,"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."}}