{"id":"W561945916","doi":"","title":"Railroads Continue Big Spending on M/W","year":2007,"lang":"en","type":"article","venue":"Railway track and structures","topic":"Transport and Economic Policies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Track (disk drive); Freight trains; Engineering; Transport engineering; Rail transportation; Ballast; Chart; Geography; Archaeology; Electrical engineering; Train; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002122846,0.0001832038,0.0002060025,0.0002098323,0.0001970346,0.0001932379,0.000123101,0.0000810417,0.0002285099],"category_scores_gemma":[0.00001336536,0.0001542997,0.00006565516,0.0001030214,0.00007978628,0.0002858475,0.00002316128,0.0001303532,0.00006696233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001045946,"about_ca_system_score_gemma":0.000003641547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002518919,"about_ca_topic_score_gemma":0.0004634761,"domain_scores_codex":[0.9990884,0.00000173306,0.0002287687,0.0002325098,0.00009662705,0.0003519589],"domain_scores_gemma":[0.999724,0.00003586235,0.00009304804,0.0001106799,0.00001298795,0.00002343534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0003743446,0.00005443421,0.2470222,0.0002029542,0.00009930568,0.00007010888,0.0007209768,0.00005696213,0.004715735,0.4550739,0.005809164,0.2858],"study_design_scores_gemma":[0.000595927,0.00001149736,0.7842923,0.000017597,0.00002567435,0.000005416286,0.0002505214,0.00003866393,0.0008523355,0.01114799,0.2025022,0.0002599132],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9353009,0.0001188906,0.00007440773,0.0002108098,0.0005795563,0.00008858836,0.000003877458,0.00009806158,0.06352494],"genre_scores_gemma":[0.9959931,0.00001468696,0.00005648404,0.001208335,0.002141016,0.00000130768,0.00001362646,0.00002019631,0.000551276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5372701,"threshold_uncertainty_score":0.6292159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01820268724739919,"score_gpt":0.224660911884441,"score_spread":0.2064582246370419,"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."}}