{"id":"W4281864574","doi":"10.1016/j.jqsrt.2022.108293","title":"Enhancing optical quantification of combustion products using thermochemical manifold reduction","year":2022,"lang":"en","type":"article","venue":"Journal of Quantitative Spectroscopy and Radiative Transfer","topic":"Oil, Gas, and Environmental Issues","field":"Energy","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Deutsche Forschungsgemeinschaft","keywords":"Hyperspectral imaging; Methane; Manifold (fluid mechanics); Combustion; Reduction (mathematics); Carbon dioxide; Plume; Carbon fibers; Environmental science; Process engineering; Computer science; Chemistry; Biological system; Mathematics; Algorithm; Physics; Artificial intelligence; Composite number; Meteorology; Engineering; Physical chemistry; Mechanical 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005216774,0.000151896,0.0003500359,0.000162014,0.0002069179,0.00001554096,0.00009521218,0.0000460368,0.0001481052],"category_scores_gemma":[0.00003645149,0.0001359674,0.00009740423,0.0002245618,0.0002021562,0.0002899175,0.00001482078,0.0003419027,0.000001545413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001676554,"about_ca_system_score_gemma":0.00004382634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000561927,"about_ca_topic_score_gemma":0.000005877705,"domain_scores_codex":[0.9984902,0.0002484847,0.0005345328,0.0002008829,0.000351544,0.0001743196],"domain_scores_gemma":[0.9994659,0.00009806209,0.0002011367,0.00008769364,0.00008475778,0.00006246757],"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":[0.0007291344,0.0002654739,0.0001744733,0.00004794548,0.0001173664,0.000006321407,0.004318665,0.004550911,0.9515507,0.03804021,0.00001311034,0.0001856831],"study_design_scores_gemma":[0.0006840545,0.001169723,0.003190083,0.00004621295,0.0001368248,0.00009296456,0.006500937,0.000557225,0.9859139,0.00145538,0.0001078192,0.0001448629],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9823325,0.001296347,0.0153932,0.0003077748,0.0002862067,0.0001299595,0.00000883925,0.000007712811,0.0002374597],"genre_scores_gemma":[0.9888093,0.0003773239,0.01061533,0.00001506204,0.0001252088,0.000001929686,0.000008039582,0.00002100499,0.00002681049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03658482,"threshold_uncertainty_score":0.5544591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03347623612687866,"score_gpt":0.2900628267512669,"score_spread":0.2565865906243882,"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."}}