{"id":"W4365140751","doi":"","title":"Automatic classification of time series of patients with chronic pain","year":2023,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Agence Nationale de la Recherche","keywords":"Series (stratigraphy); Computer science; Chronic pain; Time series; Artificial intelligence; Medicine; Machine learning; Physical therapy; Geology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008217557,0.000422277,0.0007942556,0.0002909146,0.0002953298,0.0002687278,0.001968822,0.0002628054,0.0003345111],"category_scores_gemma":[0.001574403,0.0004118435,0.0003099329,0.001376881,0.0006825469,0.0004882891,0.001445606,0.0003973189,0.00008201173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001939452,"about_ca_system_score_gemma":0.000591827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001313601,"about_ca_topic_score_gemma":0.001763397,"domain_scores_codex":[0.9907258,0.005827516,0.001277605,0.0008980256,0.0008243188,0.0004467874],"domain_scores_gemma":[0.9885346,0.001815303,0.002229606,0.002774685,0.004488306,0.0001575245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004952922,0.002104324,0.05513794,0.002872639,0.0008968054,0.000004870696,0.02098752,0.00278222,0.004215085,0.3734387,0.00108897,0.5364214],"study_design_scores_gemma":[0.0005722672,0.000008380493,0.1908011,0.005423579,0.0001622474,0.000002041297,0.0001062894,0.7895013,0.007746204,0.002112235,0.003027192,0.0005371228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2761065,0.0008369102,0.704523,0.00951436,0.0002891247,0.001027095,0.0002173344,0.0003705375,0.007115094],"genre_scores_gemma":[0.8494766,0.0002809107,0.1278551,0.00001577539,0.00002685189,0.00006684956,0.0008687298,0.0000827713,0.0213264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7867191,"threshold_uncertainty_score":0.9998333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01746475995931278,"score_gpt":0.2054005023251158,"score_spread":0.187935742365803,"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."}}