{"id":"W413079876","doi":"","title":"Using Classification Trees to Build Flexible and Intuitive Winter Weather Indices","year":2009,"lang":"en","type":"article","venue":"Transportation Research Board 88th Annual MeetingTransportation Research Board","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Categorical variable; Benchmarking; Variable (mathematics); Computer science; Environmental science; Storm; Mode (computer interface); Meteorology; Geography; Machine learning; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003994367,0.0003867653,0.0004177694,0.001035484,0.0009724249,0.0002846697,0.0005381033,0.0002895978,0.001026575],"category_scores_gemma":[0.0002541273,0.0003918346,0.0001155786,0.002254232,0.0009695438,0.001333517,0.00002788232,0.000890851,0.0003607353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004135102,"about_ca_system_score_gemma":0.000133529,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009354043,"about_ca_topic_score_gemma":0.01590704,"domain_scores_codex":[0.9925776,0.0007883349,0.0009370385,0.001268697,0.003051441,0.001376853],"domain_scores_gemma":[0.997552,0.0003584366,0.0001830652,0.0004931869,0.0006734506,0.0007398756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0016807,0.0003159374,0.7674816,0.0000915643,0.0000572313,0.00005853464,0.01639075,0.001901367,0.1932546,0.003311346,0.003107324,0.01234907],"study_design_scores_gemma":[0.0007777908,0.0007514501,0.9707154,0.0001577869,0.0000270206,0.000001579491,0.00641857,0.0002948116,0.01103643,0.00240418,0.007002919,0.0004120911],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918818,0.00004177138,0.00163815,0.002242191,0.000157284,0.001740393,0.0001633581,0.0002015401,0.001933447],"genre_scores_gemma":[0.9917752,0.00007967122,0.007015462,0.0002025524,0.0001699099,0.00017666,0.000126738,0.00006168072,0.0003921339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2032338,"threshold_uncertainty_score":0.9998866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07288422574151904,"score_gpt":0.3820291688360522,"score_spread":0.3091449430945332,"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."}}