{"id":"W7002136040","doi":"","title":"Modelling Cross-Border Rail Intermodality in the Windsor-Essex Context","year":2022,"lang":"en","type":"dissertation","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Neurology and Historical Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Truck; Train; Rail network; Context (archaeology); Port (circuit theory); Rail freight transport; Horsepower; Freight trains; Container (type theory)","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","sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001165749,0.000536549,0.000847032,0.0004718193,0.002287456,0.00007762724,0.002047197,0.0005855687,0.002810247],"category_scores_gemma":[0.0004816453,0.0005667765,0.0004770815,0.001005944,0.0006294482,0.0006515488,0.0004815446,0.002920969,0.0001318996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003652478,"about_ca_system_score_gemma":0.0001729942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006263905,"about_ca_topic_score_gemma":0.002228249,"domain_scores_codex":[0.9952481,0.001186933,0.0005038435,0.001282543,0.001089053,0.000689532],"domain_scores_gemma":[0.9973148,0.0009162091,0.0006604,0.0008353735,0.0001476633,0.0001256191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.05130591,0.009511762,0.3134108,0.004430434,0.001219015,0.007746001,0.4436243,0.0223712,0.07412811,0.03887863,0.009833461,0.0235403],"study_design_scores_gemma":[0.0113218,0.001679972,0.4984713,0.0005584366,0.0008635444,0.0001949798,0.04542904,0.001307315,0.01026143,0.01022479,0.4151407,0.004546625],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.974287,0.0007048902,0.00002205487,0.0006765908,0.001064305,0.0005953687,0.0001424053,0.0000729224,0.02243447],"genre_scores_gemma":[0.9716907,0.0002328409,0.00002089212,0.0006986737,0.00006632489,0.000007671252,0.0001209808,0.00005058003,0.02711139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4053073,"threshold_uncertainty_score":0.9996784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04583752871170656,"score_gpt":0.3209905330168091,"score_spread":0.2751530043051025,"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."}}