{"id":"W628182453","doi":"","title":"CN : A CULTURE OF PRECISION","year":2004,"lang":"en","type":"article","venue":"Progressive railroading","topic":"Transport and Economic Policies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Train; Terminal (telecommunication); DOCK; Transport engineering; Track (disk drive); Scheduling (production processes); Freight trains; Rail transit; Computer science; Operations research; Engineering; Telecommunications; Operations management; Operating system; Marine engineering; Geography","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":[0.0001073623,0.000121831,0.0001816212,0.0001323634,0.00009606741,0.00007690369,0.0001751171,0.00005498857,0.0001088241],"category_scores_gemma":[0.00003655249,0.0001019783,0.00008709243,0.0001893085,0.00006247439,0.0007459498,0.00005295801,0.00007609972,0.0001111203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002295132,"about_ca_system_score_gemma":0.0000130652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000100148,"about_ca_topic_score_gemma":0.00001419969,"domain_scores_codex":[0.9992844,0.000001400971,0.0002221739,0.0001673853,0.000102543,0.0002220602],"domain_scores_gemma":[0.9995679,0.000006869212,0.0002157294,0.000123931,0.00007490598,0.00001070179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004056789,0.00106912,0.4416632,0.00184325,0.0004222304,0.0001385732,0.01241646,0.00279112,0.02327548,0.2979336,0.00668086,0.2113604],"study_design_scores_gemma":[0.01134725,0.0001783372,0.2621597,0.003456322,0.0005327759,0.00005295273,0.004822281,0.001517979,0.03506149,0.2041906,0.4739335,0.002746849],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9582269,0.000636436,0.0002670131,0.0003146711,0.0002601476,0.0002301856,0.000002620775,0.0001217756,0.03994029],"genre_scores_gemma":[0.9984947,0.00001010962,0.0005616599,0.000230815,0.0005369006,0.00001652373,0.00001237596,0.00001577184,0.000121166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4672526,"threshold_uncertainty_score":0.4158555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01560400744105245,"score_gpt":0.2343392233054211,"score_spread":0.2187352158643686,"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."}}