{"id":"W652916652","doi":"","title":"Network Connectivity, Commodities, and the Adoption of Heavy Axle Loading by Short-line Railroads in Canada","year":2015,"lang":"en","type":"article","venue":"Transportation Research Board 94th Annual MeetingTransportation Research Board","topic":"Transport and Economic Policies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Commodity; Productivity; Line (geometry); Axle; Class (philosophy); Government (linguistics); Commodity market; Business; Industrial organization; Transport engineering; Engineering; Computer science; Economics; Finance; Mathematics; Economic growth; Artificial intelligence","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":[0.008845235,0.0003580851,0.0007395147,0.0007010504,0.0005497396,0.0002016388,0.0006069169,0.0001747169,0.00007339293],"category_scores_gemma":[0.0002793868,0.0003197809,0.0001103522,0.001856938,0.0009908768,0.00143322,0.00002966493,0.001204594,0.00001693992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003805537,"about_ca_system_score_gemma":0.001129557,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9466937,"about_ca_topic_score_gemma":0.9749652,"domain_scores_codex":[0.994022,0.0003582324,0.001322839,0.0006696494,0.002187042,0.0014402],"domain_scores_gemma":[0.9966248,0.0009218236,0.0002230421,0.0004090376,0.001629598,0.000191692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005764158,0.0002482159,0.86879,0.0008805035,0.0001202258,0.00007303074,0.005335798,0.03004483,0.0001680734,0.04818561,0.0385926,0.001796982],"study_design_scores_gemma":[0.01203445,0.0002934085,0.8408783,0.0007024802,0.0001247563,0.000001128759,0.05572868,0.0209658,0.0003370056,0.01781381,0.0498861,0.001234096],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907135,0.0006848721,0.0002242852,0.004271759,0.0002394671,0.001387907,0.0002301375,0.00006695897,0.002181093],"genre_scores_gemma":[0.9977763,0.0002080957,0.000084607,0.0003516384,0.000412427,0.0002502697,0.0007038548,0.00006280299,0.0001500121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05039288,"threshold_uncertainty_score":0.9999254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06852565812951934,"score_gpt":0.3062356588377619,"score_spread":0.2377100007082426,"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."}}