{"id":"W4237047931","doi":"10.1139/cjce-2016-0199","title":"Experimental monitoring and numerical modeling of thermal regime of railway track structure - preliminary results and conclusions","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":0,"is_retracted":true,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Track (disk drive); Thermal; Environmental science; Engineering; Meteorology; Mechanical engineering; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":{"nature":"Retraction","reason":"Notice - Limited or No Information;","date":"3/16/2017 0:00","openalex_flagged":true},"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003464146,0.0002775929,0.0002598097,0.000168582,0.0004138768,0.0002381377,0.0005567396,0.0003986653,0.001632004],"category_scores_gemma":[0.0005036274,0.0001648058,0.0002976504,0.0002354728,0.0003786691,0.0004248599,0.0002331558,0.0002549551,0.0001652128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002905534,"about_ca_system_score_gemma":0.0001975085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003438664,"about_ca_topic_score_gemma":0.002479931,"domain_scores_codex":[0.9998212,0.00003071562,0.00001146523,0.00004418185,0.00006366909,0.00002881627],"domain_scores_gemma":[0.9996982,0.0001143582,0.000037973,0.00007343084,0.00005314878,0.0000229647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008753199,0.0008022466,0.02154951,0.0001101216,0.00001725014,0.0001123921,0.0002417503,0.04186723,0.9231703,0.000377886,0.0001690882,0.01070677],"study_design_scores_gemma":[0.0001086109,0.001595389,0.05745772,0.00001088859,0.00004236466,0.0001141889,0.0001793134,0.2267997,0.7127529,0.0002246343,0.0006785414,0.00003584274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914356,0.00004396821,0.007242844,0.00002073727,0.000006331516,0.00001903525,0.000126777,0.0001145696,0.0009900214],"genre_scores_gemma":[0.9984697,0.00002234261,0.001210131,0.000002483815,0.000002201965,0.00001046133,0.00005454646,0.000007069376,0.0002211531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003438664,"threshold_uncertainty_score":0.006837249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007063395611519102,"score_gpt":0.191687642760056,"score_spread":0.1846242471485368,"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."}}