{"id":"W4379194000","doi":"10.1016/j.jqsrt.2023.108690","title":"High-temperature absorption cross-sections and interference-immune sensing method for formaldehyde near 3.6-µm","year":2023,"lang":"en","type":"article","venue":"Journal of Quantitative Spectroscopy and Radiative Transfer","topic":"Spectroscopy and Laser Applications","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Absorbance; Formaldehyde; Absorption (acoustics); Materials science; Absorption band; Interference (communication); Analytical Chemistry (journal); Range (aeronautics); Argon; Intensity (physics); Shock wave; Combustion; Absorption spectroscopy; Wavelength; Molecular physics; Chemistry; Optics; Optoelectronics; Thermodynamics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0005664221,0.00025946,0.000485814,0.0002037193,0.0005859137,0.0002104257,0.0001119427,0.000166281,0.00008507386],"category_scores_gemma":[0.00007990752,0.0002181424,0.0001863781,0.0003739569,0.0002763454,0.0004654044,0.00001550648,0.0005554308,0.000005176763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006583418,"about_ca_system_score_gemma":0.00009677963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003527468,"about_ca_topic_score_gemma":0.00003567825,"domain_scores_codex":[0.9985412,0.00007115174,0.0005842164,0.0002915562,0.0001741316,0.0003377642],"domain_scores_gemma":[0.9986051,0.0006828467,0.0001679479,0.0001248378,0.0002784897,0.0001407325],"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":[0.0008428283,0.00006147306,0.0003623853,0.0001480461,0.0003050368,0.000007242366,0.004165067,0.0006581793,0.9689067,0.02404247,0.0001604174,0.0003401326],"study_design_scores_gemma":[0.002585932,0.0008412634,0.01142329,0.0001752291,0.0002178721,0.00009976859,0.00286906,0.008870999,0.961321,0.0103177,0.0009355353,0.0003423376],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8447316,0.0005875026,0.1532103,0.000733054,0.0001592631,0.0001814705,0.000179922,0.00004794669,0.000168981],"genre_scores_gemma":[0.9482051,0.001209447,0.04997832,0.00006792615,0.0002308082,0.0000198049,0.00004033686,0.00003903236,0.0002092679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1034735,"threshold_uncertainty_score":0.8895588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0245322210282808,"score_gpt":0.352817653532467,"score_spread":0.3282854325041862,"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."}}