{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004347223,0.002522964,0.001460365,0.002835486,0.000451168,0.001240619,0.002194813,0.001814777,0.002701419],"category_scores_gemma":[0.01096582,0.001038154,0.001631943,0.00273699,0.0004796953,0.001886548,0.001297685,0.002132422,0.002474495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005843897,"about_ca_system_score_gemma":0.001054927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004526281,"about_ca_topic_score_gemma":0.004364348,"domain_scores_codex":[0.9972805,0.000527832,0.0001697025,0.0009848649,0.0009012684,0.0001359349],"domain_scores_gemma":[0.9969366,0.001447849,0.0003568934,0.0004605915,0.000768444,0.00002964804],"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":[0.0002561926,0.0003648488,0.00643617,0.00123455,0.0004257778,0.0002745773,0.0006070355,0.1124288,0.5009272,0.004497786,0.0050567,0.3674905],"study_design_scores_gemma":[0.00002395807,0.0001568208,0.003371499,0.00006708164,0.00007254319,0.0002750853,0.0001041669,0.7289398,0.2513634,0.002146943,0.01331164,0.0001671442],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02760433,0.0005402259,0.9634048,0.0001373229,0.00004443372,0.00007073975,0.0003964193,0.007038709,0.0007631129],"genre_scores_gemma":[0.08799814,0.000689052,0.9067803,0.0001232813,0.00002353805,0.0001702892,0.0008444285,0.001584473,0.001786473],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004526281,"threshold_uncertainty_score":0.02299058,"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."}}