{"id":"W3004541699","doi":"10.1049/iet-its.2019.0158","title":"Analysis of overlapping origin–destination pairs between bus stations to enhance the efficiency of bus operations","year":2020,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Ministry of Land, Infrastructure and Transport","keywords":"Computer science; Transport engineering; Computer network; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005176323,0.000581023,0.0006313918,0.002616561,0.0004463868,0.0009892229,0.0004703953,0.0002708632,0.001446254],"category_scores_gemma":[0.00209375,0.0002801511,0.000702789,0.003125431,0.0002644881,0.0008616902,0.000875004,0.0002543566,0.0002325389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005579574,"about_ca_system_score_gemma":0.0006094092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008762949,"about_ca_topic_score_gemma":0.007413235,"domain_scores_codex":[0.9991624,0.0001815819,0.00006107396,0.0001626832,0.0002840079,0.0001482907],"domain_scores_gemma":[0.9988356,0.0004965277,0.0002257667,0.0001206624,0.0002491858,0.00007222307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008904866,0.0003774681,0.5141791,0.0003263261,0.000423286,0.001416752,0.001206718,0.3449365,0.01346797,0.005574638,0.001128052,0.1160727],"study_design_scores_gemma":[0.00002183206,0.0002454683,0.2217566,0.00002148057,0.0001625054,0.0003639229,0.002148627,0.765043,0.005160475,0.002212396,0.00281612,0.0000474967],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9527388,0.00008619951,0.04455384,0.00001891063,0.000006396979,0.00006113854,0.000487687,0.00007515588,0.001971876],"genre_scores_gemma":[0.9893628,0.0000412857,0.009587764,0.00000237571,0.000004113072,0.00002884761,0.000629199,0.000008905808,0.0003346638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008762949,"threshold_uncertainty_score":0.01742393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04836147317387864,"score_gpt":0.3362075803102233,"score_spread":0.2878461071363447,"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."}}