{"id":"W4412978674","doi":"10.1016/j.chemolab.2025.105500","title":"Optimizing non-dispersive infrared channels for derived cetane number prediction: Impact of spectral resolution and feature selection","year":2025,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"DEVCOM Army Research Laboratory; Army Research Laboratory; Canadian Orthopaedic Trauma Society","keywords":"Cetane number; Feature selection; Selection (genetic algorithm); Feature (linguistics); Resolution (logic); Chemometrics; Infrared; Pattern recognition (psychology); Computer science; Artificial intelligence; Chemistry; Physics; Optics; Machine learning","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":[0.00006870874,0.0001754462,0.0002920408,0.0003442374,0.00006019193,0.00004099207,0.00007080931,0.0002428905,0.000002431482],"category_scores_gemma":[0.0001986351,0.0001648248,0.00005342151,0.001642234,0.00005908741,0.0001290675,0.00002593417,0.0001707964,4.136146e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002798391,"about_ca_system_score_gemma":0.0000123139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000548854,"about_ca_topic_score_gemma":3.354717e-7,"domain_scores_codex":[0.9992364,0.000005642738,0.0002474567,0.0002072815,0.00009080693,0.0002124359],"domain_scores_gemma":[0.9994046,0.00008706219,0.00007282757,0.0001197122,0.000259827,0.00005596648],"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":[0.00009351056,0.00004768438,0.006495732,0.001608197,0.0005079266,0.00000181233,0.0004295211,0.07018051,0.9130636,0.0006698494,0.005722317,0.001179287],"study_design_scores_gemma":[0.0003738962,0.0001507627,0.0005447503,0.0001821365,0.00004676119,0.000007173723,0.0006808231,0.1387977,0.8570251,0.0001631334,0.001818305,0.0002095036],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9246733,0.006627375,0.06698063,0.00001367868,0.0004804863,0.0005257826,0.0001335312,0.0002211996,0.0003439946],"genre_scores_gemma":[0.9969333,0.001281977,0.001525227,0.000003767604,0.00007365796,0.00003641915,0.00001763055,0.00001936107,0.0001086617],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07225998,"threshold_uncertainty_score":0.6721358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0091488416382635,"score_gpt":0.2456916639040393,"score_spread":0.2365428222657758,"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."}}