{"id":"W4405532748","doi":"10.1039/d4em00602j","title":"Speciating volatile organic compounds in indoor air: using <i>in situ</i> GC to interpret real-time PTR-MS signals","year":2024,"lang":"en","type":"article","venue":"Environmental Science Processes & Impacts","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of Standards and Technology; Canada Research Chairs; Alfred P. Sloan Foundation","keywords":"Environmental chemistry; Environmental science; Gas chromatography–mass spectrometry; In situ; Indoor air; Volatile organic compound; Chemistry; Chromatography; Mass spectrometry; Environmental engineering; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003147517,0.0007849737,0.0002903515,0.0005181787,0.0005012151,0.0008158953,0.0005293556,0.0005937022,0.001863578],"category_scores_gemma":[0.0004756217,0.0002015999,0.0002747675,0.0004704341,0.0005079214,0.0005595618,0.000300514,0.0005978682,0.0006476584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004033041,"about_ca_system_score_gemma":0.0003683327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003526763,"about_ca_topic_score_gemma":0.01105421,"domain_scores_codex":[0.9996436,0.00002966256,0.00001113422,0.0001909527,0.00008044135,0.00004434816],"domain_scores_gemma":[0.9998541,0.00004181412,0.00002795323,0.00001672433,0.00004907315,0.00001022323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008731534,0.00001838975,0.003582391,0.00007672101,0.00001940033,0.00005730546,0.00008675151,0.0001963106,0.9882687,0.0001002148,0.0001207197,0.007385731],"study_design_scores_gemma":[0.000003494109,0.00008995936,0.01432764,0.00001436381,0.00003928579,0.0001694167,0.0002123262,0.003131327,0.9801329,0.0001844384,0.001677,0.00001784675],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8666738,0.001800528,0.1179398,0.0002947681,0.0002921202,0.0001594562,0.002803671,0.001258138,0.008777649],"genre_scores_gemma":[0.9489127,0.0007859435,0.04618462,0.0003479973,0.00004599884,0.00008828623,0.0007546076,0.0002442173,0.002635397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003526763,"threshold_uncertainty_score":0.007012486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006772454614816643,"score_gpt":0.235414686811704,"score_spread":0.2286422321968873,"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."}}