{"id":"W3046090113","doi":"10.1002/cjce.23849","title":"Mid‐infrared spectroscopy as a tool for real‐time monitoring of ethanol absorption in glycols","year":2020,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Partial least squares regression; Calibration; Analyte; Fourier transform infrared spectroscopy; Diethylene glycol; Chemistry; Analytical Chemistry (journal); Absorption (acoustics); Infrared spectroscopy; Materials science; Chromatography; Mathematics; Organic chemistry; Statistics; Chemical engineering; Ethylene glycol; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001362203,0.0001351392,0.0003305927,0.0001323564,0.00002396828,0.00002866773,0.0003314085,0.0001187761,0.0002108345],"category_scores_gemma":[0.000662329,0.0001198323,0.0001512109,0.0003865323,0.00003361676,0.00007765797,0.00001143913,0.0003552505,0.000003010746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002951765,"about_ca_system_score_gemma":0.0002800535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003565463,"about_ca_topic_score_gemma":0.000004756341,"domain_scores_codex":[0.9990085,0.000004282275,0.0004202531,0.0001061755,0.000167242,0.0002935235],"domain_scores_gemma":[0.9991969,0.0001651628,0.0001666506,0.0001207184,0.000081344,0.0002692189],"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":[0.00005707413,0.000006539288,0.0005746465,0.00009611155,0.00006304863,0.00001311091,0.0003484399,0.001211481,0.9974484,0.00003011139,0.0001087203,0.00004235451],"study_design_scores_gemma":[0.0005067309,0.00003807867,0.00005998701,0.0001008152,0.00006823991,0.0000193259,0.00004596572,0.001232207,0.9975184,0.0001402878,0.0001536582,0.0001163519],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983765,0.0002976576,0.0001930995,0.0004609365,0.00004945206,0.00005980027,0.00001039521,0.00001255574,0.0005395807],"genre_scores_gemma":[0.9977088,0.00001182394,0.001741359,0.0000249793,0.0004139478,0.000005841818,0.000002410868,0.00002457711,0.00006624515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001548259,"threshold_uncertainty_score":0.4886621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01361247577977203,"score_gpt":0.243566811700243,"score_spread":0.2299543359204709,"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."}}