{"id":"W4318147813","doi":"10.1109/bigdata55660.2022.10021073","title":"Systematic Analysis of Public Transit Data Availability in Canada","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Big Data (Big Data)","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Public transport; Computer science; Transit (satellite); Transport engineering; Data science; Engineering","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":[0.02141101,0.0009038795,0.001176173,0.0196394,0.005032693,0.004196256,0.00305268,0.0006851759,0.002207376],"category_scores_gemma":[0.1030733,0.0009335437,0.001340445,0.05423275,0.001996239,0.00130056,0.003752584,0.001289694,0.0004651565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07039544,"about_ca_system_score_gemma":0.2171152,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.994211,"about_ca_topic_score_gemma":0.995789,"domain_scores_codex":[0.9609146,0.004096286,0.004018577,0.004524271,0.02342962,0.003016848],"domain_scores_gemma":[0.8352763,0.02502377,0.0134646,0.009590514,0.1131212,0.003523588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000383519,0.00009560536,0.7346997,0.006077922,0.001568215,0.0006033641,0.007399533,0.003689566,0.001140749,0.008620101,0.1499504,0.08577131],"study_design_scores_gemma":[0.00006553488,0.00004990445,0.7944499,0.004139308,0.0007057446,0.0002063171,0.005833281,0.005428701,0.001723728,0.0009410449,0.1861723,0.0002842493],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2736846,0.01551597,0.01005129,0.004478617,0.0003418226,0.0016984,0.6749789,0.000739411,0.01851103],"genre_scores_gemma":[0.6276519,0.008442282,0.01598907,0.001322008,0.00006796268,0.001816734,0.3389808,0.0003664286,0.005362959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07039544,"threshold_uncertainty_score":0.5107571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2616339857047169,"score_gpt":0.3015119091661445,"score_spread":0.0398779234614276,"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."}}