{"id":"W3116365506","doi":"10.18757/ejtir.2021.21.1.4939","title":"Estimating impacts of covid19 on transport capacity in railway networks","year":2021,"lang":"en","type":"preprint","venue":"European journal of transport and infrastructure research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Transport Canada","funders":"","keywords":"Train; Distancing; Transport engineering; Public transport; Computer science; Transport network; Scheduling (production processes); Passenger transport; Coronavirus disease 2019 (COVID-19); Operations research; Engineering; Operations management; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00723963,0.000236554,0.0006129021,0.0006051774,0.0002393696,0.00007169376,0.0004715718,0.0002280131,0.0001206483],"category_scores_gemma":[0.0002139121,0.0002159549,0.0001965198,0.0005677263,0.000494249,0.0002314929,0.00001698887,0.002735017,3.204788e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009764569,"about_ca_system_score_gemma":0.0008160581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004837015,"about_ca_topic_score_gemma":0.000618958,"domain_scores_codex":[0.9955681,0.001250972,0.001158648,0.000321031,0.001241393,0.0004598665],"domain_scores_gemma":[0.9978883,0.0002256797,0.0005837219,0.0002059707,0.0007827068,0.0003136418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000350859,0.00009049429,0.2916818,0.0004158912,0.00009675168,0.0007544648,0.0439237,0.657163,0.00008992536,0.0002559428,0.0001556694,0.005021567],"study_design_scores_gemma":[0.0009651359,0.0002493512,0.9908196,0.002631625,0.00005353592,0.00001313405,0.002644822,0.00131448,0.00004958458,0.0001969696,0.0008053043,0.00025645],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9810867,0.0005833079,0.01319822,0.0002868517,0.0005998584,0.0002397361,0.0000315095,0.0000144118,0.003959383],"genre_scores_gemma":[0.990499,0.001327487,0.007577126,0.0000315645,0.0004250905,9.612577e-7,0.00007819732,0.00003461026,0.00002601263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6991379,"threshold_uncertainty_score":0.9995657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0611958357413426,"score_gpt":0.3395944200312778,"score_spread":0.2783985842899351,"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."}}