{"id":"W4402557858","doi":"10.1021/acsestair.4c00125","title":"Air Quality Sensor Experts Convene: Current Quality Assurance Considerations for Credible Data","year":2024,"lang":"en","type":"article","venue":"ACS ES&T Air","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Office of Research and Development; U.S. Environmental Protection Agency","keywords":"Quality assurance; Quality (philosophy); Current (fluid); Computer science; Environmental science; Data science; Engineering; Electrical engineering; Operations management; External quality assessment; Physics","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":[0.00173985,0.0002246681,0.0002793782,0.00002987938,0.0004049979,0.00009789802,0.0003648421,0.00009769512,0.000211918],"category_scores_gemma":[0.001375739,0.0002115181,0.00009360248,0.0001889148,0.0002307109,0.0005782006,0.0003887219,0.0002075779,0.0002265455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001538677,"about_ca_system_score_gemma":0.00007187483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004913319,"about_ca_topic_score_gemma":0.0001674667,"domain_scores_codex":[0.9974924,0.0002405519,0.0005897298,0.0008081974,0.000442811,0.0004263622],"domain_scores_gemma":[0.9970523,0.001666622,0.0001206369,0.0009792482,0.00003011152,0.0001511276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001413125,0.0008575998,0.08483291,0.001789777,0.0002536536,0.00004014321,0.01043974,0.005093047,0.02580861,0.01824093,0.7297896,0.1227126],"study_design_scores_gemma":[0.001190408,0.0001586029,0.06883682,0.0004911569,0.0001083379,0.00003389848,0.001830507,0.01808345,0.0187524,0.01184867,0.8770654,0.001600339],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9430542,0.002557371,0.03386783,0.00843907,0.005385725,0.001162117,0.001671534,0.0009824997,0.00287968],"genre_scores_gemma":[0.9915385,0.0000454465,0.006427707,0.0002911145,0.0006014711,0.00008312739,0.0001493633,0.00003068141,0.0008325615],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1472758,"threshold_uncertainty_score":0.8625457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2027032104877716,"score_gpt":0.4044012745764757,"score_spread":0.2016980640887041,"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."}}