{"id":"W2150559728","doi":"10.1109/tits.2011.2165281","title":"Interpolating Sparse GPS Measurements Via Relaxation Labeling and Belief Propagation for the Redeployment of Ambulances","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Global Positioning System; Inference; Computer science; Real-time computing; Graph; Flow network; Key (lock); Relaxation (psychology); Data mining; Simulation; Artificial intelligence; Theoretical computer science; Mathematics; Mathematical optimization; Computer security; Telecommunications","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":[0.001644753,0.0005898929,0.0006479655,0.00106403,0.0004517094,0.0006824879,0.001258836,0.0007936141,0.0004417401],"category_scores_gemma":[0.008059757,0.0006089987,0.0004876632,0.001156981,0.0007281778,0.001385116,0.000802217,0.001295772,0.0001632687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000935163,"about_ca_system_score_gemma":0.001296967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02786326,"about_ca_topic_score_gemma":0.01926117,"domain_scores_codex":[0.9995091,0.0002032355,0.00002090289,0.00009982628,0.0001038269,0.00006322084],"domain_scores_gemma":[0.9972608,0.001705233,0.0003406522,0.0002884695,0.0003388491,0.00006603684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001734587,0.00004993413,0.001963758,0.00002081449,0.00002122142,0.00002033103,0.000070509,0.9517696,0.001173727,0.001641787,0.0003579612,0.04273679],"study_design_scores_gemma":[0.000004863736,0.00000717409,0.0001243949,9.355095e-7,0.000002024588,0.000002138737,0.000004887045,0.9989392,0.0003208996,0.00053891,0.0000520031,0.000002554232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1327309,0.0001579598,0.8651819,0.0002693392,0.00002591155,0.00004112524,0.0001222795,0.0008174375,0.0006531964],"genre_scores_gemma":[0.7767884,0.00009306137,0.2219807,0.00005217636,0.00002374272,0.00004778065,0.0003870413,0.00006226051,0.0005649948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02786326,"threshold_uncertainty_score":0.05540216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06507852185792536,"score_gpt":0.2433225798866406,"score_spread":0.1782440580287152,"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."}}