{"id":"W4387954181","doi":"10.1139/cjce-2023-0131","title":"Pavement freezing depth estimation using hybrid deep-learning models","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Chung-Ang University","keywords":"Convolutional neural network; Deep learning; Artificial intelligence; Computer science; Focus (optics); Artificial neural network; Christian ministry; Asphalt pavement; Machine learning; Asphalt; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0002281164,0.0009558244,0.0004266317,0.0007545584,0.00015266,0.0005111066,0.0008520575,0.0005243956,0.0009199705],"category_scores_gemma":[0.000533374,0.0003746234,0.0006454508,0.0004486884,0.000211719,0.001024138,0.0005854339,0.0006990313,0.000240353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000831392,"about_ca_system_score_gemma":0.0005054452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01321181,"about_ca_topic_score_gemma":0.01875946,"domain_scores_codex":[0.9998415,0.00001347384,0.000007689006,0.00006190738,0.00003927769,0.00003605492],"domain_scores_gemma":[0.9997972,0.00004948679,0.00003830675,0.00001848744,0.00008198967,0.00001465406],"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":[0.00009280653,0.00008526759,0.005509366,0.00005634049,0.00007168668,0.00007224541,0.00003340223,0.8925357,0.01379741,0.0005169712,0.0007169926,0.08651187],"study_design_scores_gemma":[0.000001482371,0.000009201061,0.0007531269,0.000002716889,0.00000637023,0.000006194007,0.000004993969,0.9965668,0.00228601,0.0002602464,0.00009873929,0.000004125807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3576285,0.000560967,0.6358521,0.0001628728,0.00007599773,0.00003900056,0.0007946112,0.002593929,0.002292026],"genre_scores_gemma":[0.9704869,0.0001166153,0.02743351,0.00003595855,0.00001281562,0.00001997734,0.0004781607,0.00003226583,0.001383891],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01321181,"threshold_uncertainty_score":0.02626985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01542126193300376,"score_gpt":0.1916355031479895,"score_spread":0.1762142412149857,"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."}}