{"id":"W4312450941","doi":"10.2139/ssrn.4252255","title":"Highly Sensitive and Selective Detection of Benzene, Toluene, Xylene, and Formaldehyde Using Au-Coated Sno2 Nanorod Arrays for Indoor Air Quality Monitoring","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Toluene; Benzene; Nanorod; Formaldehyde; Xylene; Indoor air quality; Materials science; Quality (philosophy); Indoor air; Air quality index; Chemical engineering; Environmental chemistry; Environmental science; Chromatography; Chemistry; Nanotechnology; Organic chemistry; Environmental engineering; Meteorology; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000194664,0.0004188064,0.0004390144,0.000251355,0.0001821629,0.0003090076,0.000450773,0.0005285945,0.0005681937],"category_scores_gemma":[0.0002836242,0.0003450023,0.000234445,0.0001931975,0.0001994277,0.0003743421,0.000296941,0.000275859,0.0003204923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003032213,"about_ca_system_score_gemma":0.0001499994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009800727,"about_ca_topic_score_gemma":0.003218599,"domain_scores_codex":[0.9995766,0.00004665908,0.00002055128,0.0001552783,0.0001480499,0.00005299226],"domain_scores_gemma":[0.9998505,0.00003556288,0.00002691245,0.0000172508,0.00005363376,0.00001613761],"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":[0.00003916222,0.000006234465,0.0001184081,0.00003583307,0.000004515928,0.00002623782,0.0000142109,0.000107857,0.997352,0.00004216276,0.00005326021,0.002200242],"study_design_scores_gemma":[0.000004118551,0.0000744483,0.000856894,0.000002327316,0.00001019495,0.00006834863,0.00001803989,0.003410088,0.9947913,0.00002958137,0.000727045,0.000007620082],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9421991,0.003360262,0.04733511,0.0002066111,0.0001905259,0.00005129305,0.0004729789,0.0007052287,0.005478911],"genre_scores_gemma":[0.9676536,0.0006696369,0.02807101,0.0001237456,0.00003218427,0.00003688094,0.0002161757,0.00003329939,0.003163562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009800727,"threshold_uncertainty_score":0.002200067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01936538866482737,"score_gpt":0.2644506669540588,"score_spread":0.2450852782892314,"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."}}