{"id":"W4376855323","doi":"10.2139/ssrn.4450127","title":"Highly Sensitive and Selective Detection of Benzene, Toluene, Xylene, and Formaldehyde Using Au-Coated Sno2 Nanorod Arrays for Indoor Air Quality Monitoring","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Toluene; Benzene; Nanorod; Formaldehyde; Xylene; Indoor air; Indoor air quality; Materials science; Chromatography; Environmental chemistry; Chemistry; Nanotechnology; Environmental science; Organic chemistry; Environmental 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":[0.0001954372,0.0004210104,0.000484631,0.0002590948,0.0001936501,0.0003540319,0.0004184353,0.0005582742,0.0007030024],"category_scores_gemma":[0.0002801439,0.0003720472,0.0002345411,0.0002136723,0.0002063109,0.0003878368,0.0003181175,0.0002945154,0.0004087441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003103169,"about_ca_system_score_gemma":0.0001287691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008974822,"about_ca_topic_score_gemma":0.002560227,"domain_scores_codex":[0.9995876,0.00004142777,0.00001706765,0.0001610494,0.0001415958,0.00005132046],"domain_scores_gemma":[0.9998609,0.00003840093,0.00002205689,0.00001856372,0.00004554351,0.00001457969],"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":[0.00003823286,0.000005644206,0.0001105253,0.00003553158,0.000004307215,0.00002394476,0.00001388082,0.000118233,0.9971734,0.00004977217,0.00006332056,0.002363219],"study_design_scores_gemma":[0.00000435322,0.00006175717,0.0007987152,0.000002161818,0.000009776306,0.00006719486,0.00001524704,0.003865719,0.9942548,0.0000410685,0.0008719316,0.000007316697],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9340201,0.004002905,0.05284465,0.0002369164,0.0002429526,0.00005795078,0.0005762592,0.0008624399,0.007155832],"genre_scores_gemma":[0.9655011,0.0007593934,0.02916024,0.0001338733,0.0000431105,0.00003475578,0.0002672533,0.00004526455,0.004055022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008974822,"threshold_uncertainty_score":0.00235182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03767081879674802,"score_gpt":0.2949679805537022,"score_spread":0.2572971617569542,"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."}}