{"id":"W3011685449","doi":"10.1155/2020/3096260","title":"Lewis–Mogridge Points: A Nonarbitrary Method to Include Induced Traffic in Cost-Benefit Analyses","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Narodowe Centrum Badań i Rozwoju","keywords":"Dual (grammatical number); Investment (military); Computer science; Measure (data warehouse); Boundary (topology); Econometrics; Mathematical optimization; Economics; Operations research; Mathematics; Data mining","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":[0.005785524,0.00179564,0.001615278,0.005084726,0.0006063741,0.001624853,0.002201565,0.002014987,0.005964348],"category_scores_gemma":[0.01834073,0.000797526,0.001965732,0.002333319,0.001264335,0.003344226,0.001758473,0.002422991,0.0008394571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001064732,"about_ca_system_score_gemma":0.001219882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003280361,"about_ca_topic_score_gemma":0.002098121,"domain_scores_codex":[0.9976774,0.001089417,0.0001095966,0.0003362508,0.0005931249,0.0001942393],"domain_scores_gemma":[0.9901057,0.0073549,0.0009798736,0.0005788907,0.0007536173,0.0002270082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001958408,0.0001352971,0.005265926,0.0002335335,0.0001961376,0.000241628,0.0002282361,0.8403658,0.001803492,0.0729036,0.001886329,0.07654419],"study_design_scores_gemma":[0.00001530299,0.00006826017,0.000948441,0.00004807579,0.00002722327,0.00006206501,0.00002528015,0.9629682,0.0005742454,0.03382215,0.001402047,0.00003856729],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01755431,0.0001963549,0.9795196,0.00009992179,0.00002282002,0.0001370789,0.0002115082,0.0001865016,0.002071945],"genre_scores_gemma":[0.4693925,0.000435552,0.5245633,0.0001693802,0.0001360639,0.0008635465,0.000816161,0.0002764065,0.00334701],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005964348,"threshold_uncertainty_score":0.03059715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06051805542408263,"score_gpt":0.3881522182535013,"score_spread":0.3276341628294187,"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."}}