{"id":"W4417441839","doi":"10.1139/cjce-2025-0158","title":"Comparative analysis of machine learning models for multi-depth pavement temperature prediction","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Alberta","funders":"","keywords":"Robustness (evolution); Categorical variable; Subgrade; Reliability (semiconductor); Support vector machine; Temperature measurement; Mean radiant temperature; Predictive modelling","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01476073,0.001673447,0.001814233,0.002012592,0.0006152343,0.001458637,0.001468804,0.001455029,0.001019358],"category_scores_gemma":[0.02007042,0.0004814212,0.001566574,0.000945952,0.0005303672,0.001509808,0.001092938,0.001538898,0.0002851455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002025411,"about_ca_system_score_gemma":0.001566229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01986678,"about_ca_topic_score_gemma":0.01048459,"domain_scores_codex":[0.9966955,0.002041687,0.0002185016,0.0004746862,0.0003702895,0.0001992498],"domain_scores_gemma":[0.9730243,0.02308245,0.0007368668,0.0007377805,0.002095253,0.0003232732],"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":[0.0003323657,0.0001089412,0.009348633,0.00004956654,0.00019936,0.00003289438,0.00003473193,0.967222,0.0002217118,0.0007809771,0.00034565,0.02132328],"study_design_scores_gemma":[0.000005264427,0.00005295589,0.0009313888,0.000005582478,0.0000181201,0.000004116976,0.00001006723,0.9984344,0.0001156076,0.0003684218,0.0000486973,0.000005348003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8326387,0.003863796,0.1548456,0.001571158,0.0002015644,0.0001989021,0.0006814141,0.0009770371,0.005021666],"genre_scores_gemma":[0.9794298,0.0003921438,0.01869031,0.0001075411,0.00004792498,0.00006706064,0.0004646544,0.00003808754,0.000762578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01986678,"threshold_uncertainty_score":0.07806313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01438251619500229,"score_gpt":0.2153073099601411,"score_spread":0.2009247937651388,"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."}}