{"id":"W2941317208","doi":"10.1016/j.cpletx.2019.100029","title":"Quantification of nitric acid using photolysis induced fluorescence for use in chemical kinetic studies","year":2019,"lang":"en","type":"article","venue":"Chemical Physics Letters","topic":"Spectroscopy and Laser Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universities Space Research Association; Association of Canadian Universities for Research in Astronomy; California Institute of Technology; Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Photodissociation; Nitric acid; Fluorescence; Reproducibility; Excited state; Analytical Chemistry (journal); Chemistry; Photon; Detection limit; Absorption (acoustics); Kinetic energy; Laser-induced fluorescence; Laser; Photochemistry; Materials science; Atomic physics; Optics; Environmental chemistry; Inorganic chemistry; Chromatography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001084612,0.0006811467,0.0004001123,0.0007744004,0.0004458814,0.0006996662,0.0007559237,0.0009888889,0.001383138],"category_scores_gemma":[0.001476122,0.0003277999,0.0003862976,0.000575362,0.0004078236,0.0005792105,0.0003791163,0.0007346541,0.0006345523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006076735,"about_ca_system_score_gemma":0.0006038671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002485091,"about_ca_topic_score_gemma":0.005073421,"domain_scores_codex":[0.9991238,0.0001242585,0.00003415656,0.0003192546,0.0003183173,0.0000801556],"domain_scores_gemma":[0.9993987,0.0002240365,0.0001015055,0.00007842035,0.0001642276,0.00003310769],"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":[0.0000664561,0.00003308754,0.0006273829,0.00009372554,0.00001337074,0.00002787138,0.00004691524,0.0004484895,0.9920108,0.0002953679,0.00007510158,0.006261478],"study_design_scores_gemma":[0.000005283121,0.0001086736,0.001497316,0.00002309049,0.00001478437,0.00007286067,0.00003823085,0.005250673,0.9894755,0.0003023974,0.003194239,0.00001697758],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5278223,0.00975187,0.4467618,0.0002245906,0.0002540882,0.0006162107,0.003619696,0.001216789,0.009732737],"genre_scores_gemma":[0.7318501,0.004657928,0.2546476,0.0001576206,0.00004524217,0.0006070702,0.001565531,0.0001415422,0.00632734],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002485091,"threshold_uncertainty_score":0.005736053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05702723739810274,"score_gpt":0.3070270176954972,"score_spread":0.2499997802973944,"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."}}