{"id":"W413727926","doi":"","title":"Modeling Transit Bus Emissions using MOVES: Validation of Default Distributions and Embedded Drive Cycles with Local Data","year":2015,"lang":"en","type":"article","venue":"Transportation Research Board 94th Annual MeetingTransportation Research Board","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Range (aeronautics); Transport engineering; Mode (computer interface); Public transport; Driving cycle; Service (business); Data collection; Level of service; Environmental science; Automotive engineering; Computer science; Engineering; Statistics; Mathematics; Business; Electric vehicle","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003374404,0.0003778689,0.0005069055,0.0007909168,0.0006518952,0.0001184125,0.0006697038,0.0002966303,0.00005033235],"category_scores_gemma":[0.0001829085,0.0003520776,0.00007603223,0.001968019,0.0007452324,0.001411977,0.00002441131,0.00127489,0.000008510089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001748763,"about_ca_system_score_gemma":0.0006807172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004837205,"about_ca_topic_score_gemma":0.003235056,"domain_scores_codex":[0.9939307,0.0003935605,0.001045067,0.000810691,0.002692798,0.001127159],"domain_scores_gemma":[0.9948112,0.0003725841,0.00009402245,0.0008206951,0.002938615,0.0009629201],"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":[0.0007500235,0.0001904263,0.01482498,0.0007158011,0.0001490646,0.00005969208,0.01151847,0.9582847,0.008961977,0.0005896384,0.0006157584,0.003339512],"study_design_scores_gemma":[0.002314657,0.0004380395,0.01780157,0.0008242392,0.0001072463,0.000003748705,0.0286931,0.9350865,0.01258334,0.0006081863,0.0009162421,0.0006231699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8562933,0.0003864923,0.140181,0.0002076473,0.00005989978,0.0008302128,0.001617324,0.000194354,0.0002298121],"genre_scores_gemma":[0.9879321,0.0004035007,0.009455918,0.000006434517,0.00008540887,0.00008620968,0.001892119,0.00009309329,0.000045197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1316388,"threshold_uncertainty_score":0.9998931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1142856173804114,"score_gpt":0.3793464263947968,"score_spread":0.2650608090143854,"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."}}