{"id":"W7132146928","doi":"","title":"A rail data integration and analytics system and its application to heavy haul railway","year":2023,"lang":"en","type":"article","venue":"NPARC","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Truck; Track (disk drive); Analytics; Accelerometer; Software deployment; Revenue; Identification (biology); System integration; Data integration","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001717757,0.00009854179,0.0001142208,0.0001145231,0.00003591422,0.00004327717,0.0001107043,0.00005332601,0.000001519641],"category_scores_gemma":[0.00003570179,0.00009988126,0.000007880354,0.0003008764,0.000006324486,0.00009994506,0.00006658005,0.00007728179,0.00004673428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003343439,"about_ca_system_score_gemma":0.000005218075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003328053,"about_ca_topic_score_gemma":0.00001491922,"domain_scores_codex":[0.9994521,0.000005961345,0.0001269459,0.0001862454,0.00008887346,0.0001399164],"domain_scores_gemma":[0.999549,0.00003382632,0.00001013447,0.0002978149,0.0000208764,0.00008834049],"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":[0.00003520167,0.00002277327,0.0004364745,0.001847353,0.0001623764,0.00002074997,0.001878305,0.4872381,0.2402757,0.03448462,0.0211645,0.2124339],"study_design_scores_gemma":[0.00009184236,0.00001063183,0.0007270779,0.00004099095,0.00001182809,0.000007006682,0.00009769739,0.9953428,0.0005079538,0.00006423388,0.002990509,0.0001074345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5029264,0.0002861521,0.4881503,0.0005469612,0.0003881392,0.000567764,0.000251117,0.002165613,0.004717502],"genre_scores_gemma":[0.9976969,0.00008646074,0.001785611,0.00001152599,0.00006482325,0.00002477647,0.0001455999,0.00002597206,0.0001582667],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5081047,"threshold_uncertainty_score":0.407304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01679625281113303,"score_gpt":0.2323226634295013,"score_spread":0.2155264106183682,"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."}}