{"id":"W4393328019","doi":"10.1520/jte20230208","title":"Integrating Machine Learning for Improved Prediction of Temperature and Moisture in Pavement Granular Layers","year":2024,"lang":"en","type":"article","venue":"Journal of Testing and Evaluation","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Moisture; Materials science; Geotechnical engineering; Composite material; Computer science; Environmental science; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.0006822532,0.0006605135,0.0005550513,0.0006165415,0.0002412891,0.0005421044,0.0006421764,0.0006764982,0.0004710419],"category_scores_gemma":[0.001412707,0.0003526704,0.0005184709,0.0004420688,0.0002398807,0.0006237297,0.0004361518,0.0006223557,0.0001579707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009052451,"about_ca_system_score_gemma":0.0008250433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03154193,"about_ca_topic_score_gemma":0.01804303,"domain_scores_codex":[0.9998177,0.00003817482,0.00001102525,0.00006093684,0.00004411492,0.00002800283],"domain_scores_gemma":[0.9995448,0.0002371186,0.00004806788,0.00003209584,0.000117255,0.00002063666],"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":[0.00005900733,0.00009822601,0.003378984,0.00001643981,0.00003035743,0.00002368107,0.00001502915,0.9560171,0.003737634,0.0001399413,0.0001361625,0.03634736],"study_design_scores_gemma":[6.176884e-7,0.000003726076,0.0002203488,4.880599e-7,0.000001074068,6.865977e-7,6.516665e-7,0.9993895,0.0003437162,0.00002649682,0.00001155772,0.000001151907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6784014,0.0003101529,0.3174756,0.0001311473,0.00004484844,0.00004980031,0.0001448222,0.001873836,0.00156841],"genre_scores_gemma":[0.9791693,0.0000372514,0.02020906,0.00001474275,0.000004902017,0.00002093746,0.00007229517,0.0000163363,0.0004550996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03154193,"threshold_uncertainty_score":0.06271672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0174623572566551,"score_gpt":0.263129061333842,"score_spread":0.2456667040771869,"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."}}