{"id":"W2591408558","doi":"10.1002/xrs.2760","title":"Curve‐fitting regression: improving light element quantification with XRF","year":2017,"lang":"en","type":"article","venue":"X-Ray Spectrometry","topic":"X-ray Spectroscopy and Fluorescence Analysis","field":"Physics and Astronomy","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pfizer (Canada); Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Pfizer Canada; Pfizer","keywords":"Calibration; Partial least squares regression; Analytical Chemistry (journal); Mean squared error; Chemistry; Statistics; Root mean square; Mathematics; Regression; Physics; Chromatography","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004741992,0.0003089931,0.0003830835,0.0002204686,0.00185771,0.0004844494,0.0007547537,0.00004434354,0.000680575],"category_scores_gemma":[0.0000320239,0.0002450606,0.0001837359,0.0003379885,0.00009086607,0.0004415425,0.0001928046,0.0004319324,0.0001190407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000897879,"about_ca_system_score_gemma":0.0000853689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000289385,"about_ca_topic_score_gemma":0.00001733539,"domain_scores_codex":[0.9978382,0.00004996825,0.0003779418,0.0006578796,0.0005036566,0.0005723324],"domain_scores_gemma":[0.9976535,0.00005265809,0.0007035251,0.001355749,0.00009859021,0.0001359817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008371135,0.0006299621,0.6359267,0.00003708709,0.0003583935,0.00002189991,0.0003687032,0.0000840671,0.3358639,0.0117789,0.001525212,0.01332157],"study_design_scores_gemma":[0.003682951,0.001450787,0.3726066,0.0004623421,0.0008986631,0.00001015231,0.00612265,0.009076139,0.5918182,0.00448315,0.006843586,0.002544812],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.924805,0.0002595805,0.04804918,0.002910056,0.000445521,0.0005570449,0.00005823731,0.000153005,0.02276237],"genre_scores_gemma":[0.992121,0.000005360463,0.004910436,0.00003288481,0.0008826591,0.00005493915,0.00004804305,0.00003838426,0.001906313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.26332,"threshold_uncertainty_score":0.9994417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01138628098619416,"score_gpt":0.2720877477345178,"score_spread":0.2607014667483236,"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."}}