{"id":"W2153701318","doi":"10.3141/1870-05","title":"Transit Buses as Traffic Probes: Use of Geolocation Data for Empirical Evaluation","year":2004,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ryerson University; Portland State University","keywords":"Automatic vehicle location; Transit (satellite); Transport engineering; Induction loop; Geolocation; Computer science; Intelligent transportation system; Data collection; Metropolitan area; Software deployment; Public transport; Real-time computing; Global Positioning System; Engineering; Detector; Telecommunications; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.06817355,0.001765228,0.001283595,0.008361609,0.0007617467,0.003552177,0.002533979,0.002301565,0.00155538],"category_scores_gemma":[0.2749574,0.0006704068,0.0009637053,0.007703925,0.00209022,0.006680544,0.002689185,0.001758288,0.0004897397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001395543,"about_ca_system_score_gemma":0.001717645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01140944,"about_ca_topic_score_gemma":0.006313203,"domain_scores_codex":[0.9198574,0.07085832,0.002237973,0.002345702,0.004218508,0.0004819627],"domain_scores_gemma":[0.5782655,0.3777136,0.01381569,0.02003975,0.008927017,0.001238373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009044927,0.0007807103,0.7583554,0.0003578681,0.001080551,0.0002918487,0.001093205,0.1029978,0.0004111283,0.01035744,0.003111345,0.1202583],"study_design_scores_gemma":[0.0001570633,0.0009879223,0.1209312,0.0001691271,0.0003082127,0.0002677153,0.002122047,0.8613161,0.000674817,0.01030142,0.002688639,0.00007562995],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7992175,0.001979674,0.1867602,0.001233367,0.00009621195,0.0006085,0.003906182,0.0005032951,0.005694972],"genre_scores_gemma":[0.9653954,0.0004296966,0.03133291,0.00005959357,0.00005124022,0.0002796358,0.002122285,0.00006344356,0.0002658852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06817355,"threshold_uncertainty_score":0.3605405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2782494445710996,"score_gpt":0.4421377390641241,"score_spread":0.1638882944930246,"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."}}