{"id":"W7036368390","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":"en","type":"article","venue":"Dialnet (Universidad de la Rioja)","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; TSG101; Proteogenomics; Gestational period; Diafiltration; Hyporeflexia","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"],"consensus_categories":[],"category_scores_codex":[0.001437218,0.0003136584,0.0002868532,0.0001419459,0.0005146753,0.0002505279,0.0004122296,0.0002820765,0.0002744064],"category_scores_gemma":[0.000432526,0.0003319329,0.0001939342,0.0004264537,0.0003076915,0.0002084409,0.0001838294,0.0007267094,0.00008416139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001342268,"about_ca_system_score_gemma":0.0001704825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006553439,"about_ca_topic_score_gemma":0.0001291846,"domain_scores_codex":[0.9973608,0.0005930693,0.0002180343,0.0004727834,0.0003176373,0.001037725],"domain_scores_gemma":[0.998188,0.001056176,0.0001046532,0.0002363103,0.00001211608,0.0004027202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000163324,0.0001527083,0.8047936,0.0001845209,0.0001203033,0.002079286,0.02678458,0.02550892,0.08684684,0.0006011419,0.0007603955,0.05200437],"study_design_scores_gemma":[0.001576982,0.0005330116,0.6612628,0.0009677644,0.0004284748,0.001280572,0.01037769,0.2573801,0.03793311,0.004336982,0.02244894,0.001473611],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847912,0.0006197359,0.01214902,0.0003588932,0.0001190182,0.0001324955,0.00002872713,0.0003148628,0.001486043],"genre_scores_gemma":[0.9923197,0.0001628839,0.005848809,0.0001032328,0.0003101942,0.00001356471,0.00001370872,0.00005878508,0.001169115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2318712,"threshold_uncertainty_score":0.9999133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01538268235343544,"score_gpt":0.2788488507855243,"score_spread":0.2634661684320889,"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."}}