{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00182009,0.0005813102,0.0005004372,0.001833667,0.0005252235,0.0011884,0.0005875024,0.0005735491,0.002631311],"category_scores_gemma":[0.002763235,0.0002433314,0.0003929857,0.001544701,0.0003363226,0.001161285,0.001488428,0.0006823696,0.0008615736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005966153,"about_ca_system_score_gemma":0.001031845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006446151,"about_ca_topic_score_gemma":0.003389654,"domain_scores_codex":[0.9989896,0.0001478778,0.0001219913,0.0002596487,0.0004030672,0.0000777327],"domain_scores_gemma":[0.9989963,0.0002442321,0.0001041958,0.0001518049,0.0004120307,0.00009143048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001459451,0.0009397211,0.04898947,0.0005533468,0.0002800539,0.001957563,0.002318497,0.05998545,0.1177467,0.008294852,0.02970443,0.7277704],"study_design_scores_gemma":[0.0002268081,0.001217681,0.04112483,0.0001502247,0.0001207633,0.0008378385,0.001083864,0.8017496,0.08389094,0.005085464,0.06432822,0.0001837727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2293267,0.0004441723,0.6669124,0.001266831,0.0002328088,0.001197184,0.006465363,0.08475855,0.009395952],"genre_scores_gemma":[0.6814722,0.000315904,0.3066086,0.0003568786,0.00007156704,0.0005086441,0.006019866,0.0006627323,0.003983523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006446151,"threshold_uncertainty_score":0.01281726,"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."}}