{"id":"W1943925430","doi":"10.1111/risa.12063","title":"Transport Mode Selection for Toxic Gases: Rail or Road?","year":2013,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Hazardous waste; Truck; Transport engineering; Mode (computer interface); Mode of transport; Dangerous goods; Selection (genetic algorithm); Risk assessment; Risk analysis (engineering); Engineering; Computer science; Business; Public transport; Computer security; Waste management","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002036479,0.0002489527,0.0008901198,0.001330489,0.0005223743,0.000241467,0.0006870986,0.0001515411,0.008243284],"category_scores_gemma":[0.001143005,0.0001586703,0.001684234,0.00755362,0.00007019047,0.0006240692,0.0000243504,0.0001549714,0.000869809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006508132,"about_ca_system_score_gemma":0.00009200753,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008485173,"about_ca_topic_score_gemma":0.01778095,"domain_scores_codex":[0.9960933,0.0002859644,0.001103893,0.0008538747,0.001221145,0.0004418346],"domain_scores_gemma":[0.9968337,0.0009091258,0.0004823885,0.0008012796,0.0007314624,0.0002420696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002552396,0.0002474911,0.3890099,0.000006073013,0.004157979,0.00000293121,0.001097772,0.2038324,0.0003436434,0.0001439824,0.015072,0.3858306],"study_design_scores_gemma":[0.0004968893,0.0001020406,0.1145255,0.0000028686,0.005395913,0.000001779574,0.001108691,0.8516634,0.0003579313,0.01597333,0.0100071,0.0003646135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5388438,0.0001282836,0.4589049,0.0008667489,0.00006399701,0.0003053778,0.0001015862,0.00007502476,0.0007102637],"genre_scores_gemma":[0.9757804,0.0006640562,0.006560876,0.0001244613,0.0001257951,0.0001292499,0.00005680007,0.00001691639,0.01654148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.647831,"threshold_uncertainty_score":0.9999081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05191178467073245,"score_gpt":0.3648910209435177,"score_spread":0.3129792362727853,"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."}}