{"id":"W4401351813","doi":"10.1088/2634-4505/ad6bbf","title":"Scaling traffic variables from sensors sample to the entire city at high spatiotemporal resolution with machine learning: applications to the Paris megacity","year":2024,"lang":"en","type":"article","venue":"Environmental Research Infrastructure and Sustainability","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"Grantham Foundation for the Protection of the Environment","keywords":"Megacity; Scaling; Sample (material); Mega-; Computer science; Environmental science; Artificial intelligence; Mathematics; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008944814,0.0001726105,0.0001256592,0.0001026293,0.0007006419,0.0001752972,0.0002272709,0.00008175745,0.0001312664],"category_scores_gemma":[0.0001100721,0.0001075578,0.0000360475,0.0003620056,0.0002185703,0.0001145052,0.0003145857,0.0006335286,0.000007316133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008214389,"about_ca_system_score_gemma":0.00002649523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006157404,"about_ca_topic_score_gemma":0.0002941447,"domain_scores_codex":[0.998329,0.0002119123,0.0001857134,0.0004160945,0.0004859804,0.0003712535],"domain_scores_gemma":[0.999063,0.0003391739,0.00001331573,0.000402795,0.00002088085,0.0001608098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000252548,0.00008405195,0.0294863,0.0002841039,0.0002079858,0.000013406,0.005632448,0.6917317,0.0003112288,0.00228984,0.03053552,0.2391709],"study_design_scores_gemma":[0.0001481457,0.000134788,0.1232082,0.00001964868,0.00003100818,0.000003550403,0.002108864,0.1923892,0.0003767429,0.001909407,0.6794508,0.0002196614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8438166,0.0004005671,0.1499967,0.002851699,0.00005814507,0.001749396,0.0002774885,0.0007367239,0.0001126239],"genre_scores_gemma":[0.9979572,0.0001763429,0.001079271,0.00004504098,0.0001074024,0.0003235975,0.00014293,0.00002349522,0.0001447699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6489153,"threshold_uncertainty_score":0.5388842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008590381426996937,"score_gpt":0.2479048186143994,"score_spread":0.2393144371874025,"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."}}