{"id":"W1483427682","doi":"10.4000/bifea.5857","title":"Innovaciones tecnológicas aplicadas al transporte colectivo en Quito. Optimización en la evaluación de la demanda con GPS y SIG","year":2004,"lang":"es","type":"article","venue":"Bulletin de l’Institut français d’études andines","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Global Positioning System; Geography; Transport engineering; Cartography; Welfare economics; Humanities; Forestry; Computer science; Engineering; Telecommunications; Economics; Art","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":[0.002069336,0.0009424382,0.0009722614,0.00108046,0.000597748,0.002464283,0.001023777,0.0009222272,0.004549062],"category_scores_gemma":[0.003681558,0.0004738044,0.001165755,0.001556512,0.0004873791,0.001645493,0.001055686,0.0009046454,0.0009013459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001177235,"about_ca_system_score_gemma":0.001712381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01535713,"about_ca_topic_score_gemma":0.02124523,"domain_scores_codex":[0.9989251,0.000366159,0.00004482833,0.0002286484,0.0003271674,0.0001081809],"domain_scores_gemma":[0.9988565,0.0004305338,0.0001191384,0.0001662822,0.0003741969,0.00005324806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007041413,0.0004189733,0.03619736,0.001339501,0.0002416719,0.0002656174,0.001474885,0.2483733,0.02613389,0.01832073,0.005101819,0.6614282],"study_design_scores_gemma":[0.00021373,0.001228216,0.03895753,0.000431363,0.0004443894,0.0003741921,0.003794112,0.8191302,0.03093638,0.0262778,0.07804541,0.0001666234],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2329209,0.004513431,0.7273462,0.00187001,0.0002555077,0.0005334049,0.0008898947,0.002649761,0.02902088],"genre_scores_gemma":[0.7292299,0.003575711,0.2497705,0.000231718,0.00008060192,0.0003463115,0.001091462,0.0002539203,0.0154198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01535713,"threshold_uncertainty_score":0.03053552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009208003723731808,"score_gpt":0.2953481582295056,"score_spread":0.2861401545057737,"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."}}