{"id":"W7126530887","doi":"","title":"Arquitectura de IoT para el monitoreo de emisiones de gases contaminantes de vehículos y su validación a través de Machine Learning","year":2024,"lang":"es","type":"article","venue":"Universidad Politécnica Salesiana Repositorio Digital (Universidad Politécnica Salesiana)","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Internet of Things; Air pollution; Environmental pollution","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","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.001585491,0.001896431,0.001623922,0.001160301,0.002098554,0.003471528,0.002546268,0.001527683,0.0001912119],"category_scores_gemma":[0.0007936019,0.002228371,0.001569031,0.002174857,0.001455443,0.001828432,0.001121372,0.002795468,0.0003718579],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01227392,"about_ca_system_score_gemma":0.002509534,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01231106,"about_ca_topic_score_gemma":0.00008022524,"domain_scores_codex":[0.9883127,0.001360142,0.001374334,0.002425374,0.001486611,0.005040805],"domain_scores_gemma":[0.9920008,0.002259055,0.0008419321,0.001645954,0.0001562246,0.003096101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001586117,0.001610213,0.8098519,0.002586331,0.002788568,0.02734117,0.02679681,0.007067623,0.04539707,0.04429405,0.003946295,0.02673382],"study_design_scores_gemma":[0.00871757,0.005263893,0.4353887,0.01434864,0.006684166,0.02033444,0.08229011,0.1057046,0.02866515,0.005477798,0.2738705,0.01325434],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9707299,0.004660632,0.004142869,0.003649693,0.0007217168,0.0008029414,0.0007584811,0.001357577,0.01317619],"genre_scores_gemma":[0.9831868,0.000966061,0.002710862,0.0001868807,0.002005958,0.00002032297,0.0001535627,0.000396858,0.01037263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3744632,"threshold_uncertainty_score":0.9997686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01409976264113888,"score_gpt":0.270229003378181,"score_spread":0.2561292407370421,"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."}}