{"id":"W2984871496","doi":"10.1016/j.saa.2019.117539","title":"Determination of acidity in metal incorporated zeolites by infrared spectrometry using artificial neural network as chemometric approach","year":2019,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"McGill University","keywords":"Artificial neural network; Absorbance; Mean squared error; Feature selection; Zeolite; Fourier transform infrared spectroscopy; Biological system; Mathematics; Analytical Chemistry (journal); Chemistry; Materials science; Pattern recognition (psychology); Computer science; Artificial intelligence; Statistics; Chromatography; Engineering; Catalysis; Chemical engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003687202,0.0007677935,0.001274518,0.001120947,0.0001345806,0.0001590905,0.000595304,0.0004534103,0.0007068588],"category_scores_gemma":[0.0001733266,0.0007962791,0.0003965383,0.004966632,0.0002832408,0.0003032739,0.000259601,0.0007258937,0.00001325671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003186226,"about_ca_system_score_gemma":0.0001640319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001316152,"about_ca_topic_score_gemma":0.000005843896,"domain_scores_codex":[0.9955184,0.0001348255,0.001061947,0.001279542,0.000810342,0.001194982],"domain_scores_gemma":[0.9980079,0.00009064323,0.0006760376,0.0008238701,0.0001203771,0.0002811608],"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":[0.0002324038,0.0005232228,0.00449208,0.0001641413,0.0002135724,0.00004729953,0.00003941823,0.00003564593,0.9929976,0.001166741,0.00004383074,0.00004400225],"study_design_scores_gemma":[0.001128622,0.0003412344,0.0002614149,0.00004059544,0.000370361,0.0001044291,0.0001418061,0.006587924,0.9853206,0.004866578,0.00005689563,0.0007795422],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9813687,0.00207515,0.007736319,0.00007084984,0.00009968338,0.0004094648,0.00005474336,0.0001088773,0.008076165],"genre_scores_gemma":[0.9821414,0.0001114349,0.01693088,0.0001230568,0.0001220334,0.00002254381,0.0003230257,0.00009995067,0.0001257265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00919456,"threshold_uncertainty_score":0.9994488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01179346925099079,"score_gpt":0.2570517603101195,"score_spread":0.2452582910591287,"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."}}