{"id":"W6944404131","doi":"10.17632/s3ckgmd2st.1","title":"Data for: Integrating network science and public transport accessibility analysis for comparative assessment","year":2019,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"Urban Design and Spatial Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Public transport; Transport network; Intelligent transportation system; Network analysis; Key (lock); The Internet","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001489382,0.001897956,0.001079098,0.005809121,0.0007082212,0.002445988,0.002812005,0.001869164,0.04265612],"category_scores_gemma":[0.01055695,0.0007226521,0.00168887,0.01090453,0.0004411633,0.001768124,0.002295118,0.001881369,0.03883818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002092787,"about_ca_system_score_gemma":0.002811946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07061761,"about_ca_topic_score_gemma":0.08706834,"domain_scores_codex":[0.9984872,0.0002716925,0.0002926168,0.0003509747,0.0003808879,0.0002166425],"domain_scores_gemma":[0.9955062,0.001173397,0.0005828025,0.0009729957,0.001438451,0.0003262022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006698476,0.00003476294,0.002611793,0.001166148,0.00008054561,0.00002849786,0.00006925584,0.001015763,0.0001059116,0.001502372,0.9893261,0.003991808],"study_design_scores_gemma":[0.0002502896,0.00001539269,0.01455673,0.0007103244,0.00006963236,0.00005365209,0.0002682612,0.0009499048,0.0003279463,0.002338152,0.980412,0.00004782486],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001395763,0.00003192843,0.00007792813,0.00004767445,0.00001199581,0.000007920165,0.9991143,0.0001413984,0.0004272934],"genre_scores_gemma":[0.0006217211,0.00003807439,0.0003344834,0.00001797449,0.000004012445,0.00006536851,0.9985521,0.00004726155,0.000318988],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07061761,"threshold_uncertainty_score":0.1426989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08386509993057863,"score_gpt":0.3318554252040352,"score_spread":0.2479903252734565,"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."}}