{"id":"W4321763682","doi":"10.15353/acmla.n171.5027","title":"Transit Vector Data","year":2023,"lang":"en","type":"article","venue":"Bulletin - Association of Canadian Map Libraries and Archives (ACMLA)","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Transit (satellite); Computer science; Agency (philosophy); Public transport; File format; World Wide Web; Database; Data science; Information retrieval; Transport engineering; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0005743967,0.0008987628,0.0005480379,0.004192388,0.0009007951,0.002038402,0.001284482,0.0007181133,0.05652728],"category_scores_gemma":[0.004703448,0.0004209669,0.0005909709,0.008834957,0.0002958147,0.001295273,0.001085908,0.001356984,0.04439506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00300926,"about_ca_system_score_gemma":0.006086726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4581495,"about_ca_topic_score_gemma":0.5294727,"domain_scores_codex":[0.9990583,0.00007810834,0.00008876558,0.000182711,0.0004150858,0.0001771473],"domain_scores_gemma":[0.9966565,0.0002156129,0.0001802139,0.0006497045,0.002055581,0.0002423988],"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":[0.0001644369,0.00007786717,0.01143972,0.0004915165,0.00004935266,0.0001088812,0.0003214593,0.003519704,0.000791794,0.004300461,0.9393507,0.0393842],"study_design_scores_gemma":[0.00003247792,0.00002128192,0.01769708,0.0001442326,0.00002330428,0.00005824584,0.0004833281,0.001676353,0.0009353249,0.001043396,0.9778402,0.00004482363],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001573894,0.00005340638,0.0009198941,0.00009124839,0.00005933185,0.00007481183,0.9876246,0.0009838064,0.008618996],"genre_scores_gemma":[0.007610841,0.0001363139,0.002725659,0.00003709413,0.00001762431,0.0001251138,0.9824232,0.000214488,0.006709642],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4581495,"threshold_uncertainty_score":0.9109656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01261536091946526,"score_gpt":0.1760768012178911,"score_spread":0.1634614402984258,"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."}}