{"id":"W3001634076","doi":"10.1080/17515831.2020.1720379","title":"Improved modelling of trains braking under low adhesion conditions","year":2020,"lang":"en","type":"article","venue":"Tribology - Materials Surfaces & Interfaces","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Dynamic braking; Train; Braking system; Creep; Automotive engineering; Brake; Computer science; Integrator; Adhesion; Engineering; Materials science","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"],"consensus_categories":[],"category_scores_codex":[0.0001448808,0.0002871135,0.000544412,0.00008497557,0.00005348578,0.00004394585,0.0002599122,0.0002111234,0.0003208854],"category_scores_gemma":[0.00002403784,0.0002877765,0.00006305074,0.0001462465,0.00009813332,0.0001768885,0.00005127379,0.0001641664,0.00003820657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003535913,"about_ca_system_score_gemma":0.00001978675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002953926,"about_ca_topic_score_gemma":0.000006181283,"domain_scores_codex":[0.9986421,0.00005429264,0.0005722786,0.000266059,0.0001017164,0.0003635215],"domain_scores_gemma":[0.9994464,0.0001131592,0.0001033937,0.0001913916,0.00004931938,0.0000963172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002411205,0.000009616689,0.00000985772,0.0001420755,0.0000527763,0.000001361954,0.0003790348,0.4939949,0.505162,0.0001429412,0.00005898734,0.00002231774],"study_design_scores_gemma":[0.0003349321,0.0001014348,0.00007548604,0.00006558497,0.00003200958,0.000005035478,0.0002206297,0.4189542,0.5797981,0.0001381207,0.00004603724,0.0002284115],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9384738,0.0003640246,0.0588837,0.0001399653,0.0008267242,0.0001825808,0.0003369036,0.0005455471,0.0002468075],"genre_scores_gemma":[0.9976978,0.000146495,0.001844055,0.00004886109,0.00007443473,0.00001359775,0.00007950651,0.00006659197,0.00002866026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07504068,"threshold_uncertainty_score":0.9999574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02626399143725651,"score_gpt":0.2295098564921447,"score_spread":0.2032458650548882,"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."}}