{"id":"W2084757530","doi":"10.1142/s0218194008003751","title":"AN INTELLIGENT AGENT MOBILE EMISSIONS MODEL FOR URBAN ENVIRONMENTAL MANAGEMENT","year":2008,"lang":"en","type":"article","venue":"International Journal of Software Engineering and Knowledge Engineering","topic":"Traffic control and management","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"National Key Research and Development Program of China","keywords":"Process (computing); Agent-based model; Computer science; Simulation; Transport engineering; Engineering; Artificial intelligence","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":[0.0002869025,0.0005299288,0.0005375714,0.0004406457,0.0003850968,0.0009130486,0.001231784,0.001012298,0.001842793],"category_scores_gemma":[0.0007834056,0.0002743453,0.000558507,0.0004071011,0.0004659667,0.001223627,0.0005955166,0.0006490697,0.0002928304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008000634,"about_ca_system_score_gemma":0.0007093902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007745038,"about_ca_topic_score_gemma":0.00539285,"domain_scores_codex":[0.9998056,0.00005458049,0.000009001304,0.00005258615,0.00005250587,0.00002559457],"domain_scores_gemma":[0.9997843,0.0000935752,0.0000413181,0.00001088393,0.00005518947,0.00001473578],"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":[0.000009126654,0.00001652214,0.0003475671,0.00001530857,0.00001366943,0.00004752881,0.00003132631,0.9800521,0.0004835698,0.01600987,0.0001567069,0.002816732],"study_design_scores_gemma":[0.000003209616,0.000007630602,0.00007361462,0.000001593628,0.000003884399,0.000007584851,0.000007459821,0.9966142,0.0000761927,0.002647357,0.0005539123,0.000003415856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06613963,0.0003406523,0.9189177,0.0004019123,0.00007869128,0.00006826451,0.0001941038,0.0002255935,0.01363356],"genre_scores_gemma":[0.9162663,0.0004552765,0.069328,0.0000990461,0.00005423781,0.0003082259,0.0002049836,0.00005891496,0.01322506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007745038,"threshold_uncertainty_score":0.01539987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008515614705402127,"score_gpt":0.2122732198206591,"score_spread":0.203757605115257,"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."}}