{"id":"W2898652859","doi":"10.1016/j.trpro.2018.10.019","title":"Analysis of travel pattern changes due to a medium-term disruption on public transit networks using smart card data","year":2018,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Police Service; Bombardier (Canada)","funders":"","keywords":"Smart card; Public transport; Transit (satellite); Term (time); Transport engineering; Travel behavior; Service (business); Business; Order (exchange); Computer science; Computer security; Engineering; Marketing; Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006801394,0.0003221925,0.0002975823,0.001658599,0.0002764073,0.0007944586,0.0005457145,0.0002869167,0.000950221],"category_scores_gemma":[0.002648987,0.0001281842,0.0003226056,0.003251686,0.0002795508,0.0006765914,0.000513434,0.0004116398,0.0002403645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001251236,"about_ca_system_score_gemma":0.001010197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1759959,"about_ca_topic_score_gemma":0.2293063,"domain_scores_codex":[0.9994128,0.0001232798,0.00003552881,0.0001080943,0.0002036656,0.000116787],"domain_scores_gemma":[0.9985957,0.0003169193,0.000372246,0.0002029251,0.0004010727,0.00011125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001867283,0.0001425555,0.9446551,0.0001333308,0.0001906785,0.0003215754,0.0005363514,0.01859089,0.003356278,0.0004685414,0.00110097,0.03031699],"study_design_scores_gemma":[0.000003410718,0.0001038221,0.9602619,0.00001564035,0.00005222057,0.0000969234,0.001164665,0.03481897,0.001019275,0.0001179946,0.002327997,0.00001713811],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904611,0.00008356894,0.004038934,0.0001023596,0.00001456848,0.00006283564,0.003926497,0.00006205148,0.00124821],"genre_scores_gemma":[0.9933478,0.00009342625,0.002107491,0.00001466284,0.000009360028,0.00002954459,0.003777063,0.000006595332,0.0006139297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1759959,"threshold_uncertainty_score":0.349943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2259681003417914,"score_gpt":0.4468320742173347,"score_spread":0.2208639738755433,"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."}}