{"meta":{"query_hash":"a82e1576499f","filters":{"venue":"Intelligent Transportation Infrastructure"},"cohort_total":1,"direct_labels_cover":0,"predictions_cover":1,"exported":1,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/a82e1576499f","api":"https://metacan.xera.ac/api/v1/cohort?venue=Intelligent+Transportation+Infrastructure"},"results":[{"id":"W4372349739","doi":"10.1093/iti/liad002","title":"A review of hybrid physics-based machine learning approaches in traffic state estimation","year":2023,"lang":"en","type":"review","venue":"Intelligent Transportation Infrastructure","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"State (computer science); Computer science; Estimation; Data science; Artificial intelligence; Machine learning; Systems engineering; Engineering; Algorithm","score_opus":0.04262257114333661,"score_gpt":0.28198813946234014,"score_spread":0.23936556831900352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4372349739","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010199343,0.9679923,0.026665673,0.0004239238,0.00044203032,0.000026962429,0.00009616414,0.00009196705,0.0032409886],"genre_scores_gemma":[0.011979342,0.9712425,0.014151,0.00022910413,0.0007151858,0.00005148645,0.00020493366,0.000031449996,0.0013949724],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994863,0.00010257949,0.00006754946,0.00015606862,0.0001550984,0.00003226914],"domain_scores_gemma":[0.9983327,0.0010807348,0.00009992678,0.000059906888,0.00039078319,0.00003587275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001385007,0.0015417271,0.0015899878,0.0029576046,0.00033769727,0.0013334799,0.0016533289,0.0012306712,0.002739687],"category_scores_gemma":[0.0027840806,0.00079535996,0.001264484,0.0046047107,0.0004885696,0.0021931557,0.00087607896,0.0015694511,0.0013677068],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054347307,0.00014023541,0.0010422113,0.016710546,0.000321782,0.00012348004,0.00011917731,0.018182196,0.0014822135,0.02009705,0.015800819,0.9259261],"study_design_scores_gemma":[0.000033178687,0.00047731068,0.004373014,0.010605042,0.0011548065,0.001247912,0.00025759687,0.079425074,0.0048102215,0.036004215,0.8613206,0.00029099852],"about_ca_topic_score_codex":0.002422334,"about_ca_topic_score_gemma":0.0018565649,"teacher_disagreement_score":0.0029576046,"about_ca_system_score_codex":0.0005172485,"about_ca_system_score_gemma":0.0010175388,"threshold_uncertainty_score":0.009165227},"labels":[],"label_agreement":null}]}