{"id":"W4240802616","doi":"10.1149/ma2011-01/17/1172","title":"An Electronic Nose for the Detection of Carbonyl Species","year":2011,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Steacie Institute for Molecular Sciences","funders":"","keywords":"Electronic nose; Nose; Computer science; Artificial intelligence; Art; Chemistry; Chromatography; Biology; Paleontology","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":[0.00006995124,0.00008904531,0.00009011072,0.00002717754,0.000039815,0.000005687,0.0001662277,0.00007505276,0.00000284937],"category_scores_gemma":[0.000192621,0.0000710635,0.00003500513,0.00007238056,0.0000474554,0.00005730087,0.000009456758,0.0001592901,0.000002174119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004605033,"about_ca_system_score_gemma":0.000002182209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002805637,"about_ca_topic_score_gemma":0.00003431102,"domain_scores_codex":[0.9994371,0.000003021182,0.0001595113,0.00009525834,0.00006469456,0.0002404037],"domain_scores_gemma":[0.9995552,0.000137153,0.0000519403,0.0002087396,0.00002787539,0.00001913577],"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":[0.000007599329,0.000009015012,0.00003122689,0.00001832467,0.00001226729,3.321932e-7,0.0001108378,0.04007677,0.9581946,0.00002473792,0.00000538572,0.001508875],"study_design_scores_gemma":[0.00007320746,0.00004655639,0.002127415,0.00001108569,0.00001225027,0.000001613869,0.0001954535,0.003447509,0.9917277,0.001269116,0.001005266,0.0000828313],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892305,0.0003499695,0.0005335279,0.00001035989,0.0001077818,0.0001195111,0.000002333554,0.0004983767,0.009147624],"genre_scores_gemma":[0.9986242,0.00005326671,0.001209255,0.00000268957,0.0000570243,0.00001559188,7.92376e-7,0.0000233858,0.00001379072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03662926,"threshold_uncertainty_score":0.2897885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01667696379208904,"score_gpt":0.2184403514748918,"score_spread":0.2017633876828028,"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."}}