{"id":"W2520920210","doi":"10.1002/atr.1395","title":"Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Computer science; Travel time; Operations research; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001409735,0.0009621516,0.001167127,0.001679849,0.0009511317,0.001380903,0.001478972,0.001477897,0.001572357],"category_scores_gemma":[0.004734057,0.0008262336,0.001187891,0.00136998,0.001048105,0.001025883,0.001215131,0.0007191213,0.0001998206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002773688,"about_ca_system_score_gemma":0.001966245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03421332,"about_ca_topic_score_gemma":0.01960809,"domain_scores_codex":[0.9993601,0.0001888918,0.0000285905,0.0001536919,0.0000925114,0.0001761569],"domain_scores_gemma":[0.9961514,0.00238391,0.0006612975,0.0001251536,0.0004022652,0.0002759681],"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":[0.00002696477,0.000007831325,0.0007514104,0.000007550016,0.00001168728,0.00002800002,0.000008105617,0.9971685,0.0001632809,0.0007903461,0.00005651496,0.0009798394],"study_design_scores_gemma":[0.000001923348,0.00001042064,0.0001738501,0.000001628788,0.000003149198,0.000007689829,0.00001029026,0.9990514,0.00009005384,0.0006088756,0.00003741443,0.000003269524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5678513,0.0002790577,0.427902,0.0002675768,0.00001870914,0.0001564812,0.0004802327,0.0002662113,0.002778458],"genre_scores_gemma":[0.9576175,0.0001068743,0.03964046,0.00002586754,0.000006720003,0.00008229198,0.0004746555,0.00003211678,0.002013461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03421332,"threshold_uncertainty_score":0.06802833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01110906526713781,"score_gpt":0.2294700175072736,"score_spread":0.2183609522401358,"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."}}