{"id":"W2058250321","doi":"10.1107/s090904950904655x","title":"Development and exploration of a new methodology for the fitting and analysis of XAS data","year":2009,"lang":"en","type":"article","venue":"Journal of Synchrotron Radiation","topic":"X-ray Spectroscopy and Fluorescence Analysis","field":"Physics and Astronomy","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Center for Research Resources; Biological and Environmental Research; Natural Sciences and Engineering Research Council of Canada; British Columbia Knowledge Development Fund; National Institutes of Health; Consejo Nacional de Ciencia y Tecnología; U.S. Department of Energy","keywords":"X-ray absorption spectroscopy; Computer science; XANES; Context (archaeology); Calibration; Enhanced Data Rates for GSM Evolution; Monte Carlo method; Reverse Monte Carlo; Function (biology); Synchrotron; MATLAB; Data mining; Algorithm; Absorption spectroscopy; Spectroscopy; Physics; Optics; Mathematics; Artificial intelligence; Statistics","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":[0.004513378,0.00124408,0.001151585,0.002044932,0.0006828028,0.001907849,0.0026667,0.001185677,0.004623723],"category_scores_gemma":[0.00885712,0.0008788238,0.001185848,0.001915908,0.0006821745,0.001986852,0.001774363,0.002552127,0.002794941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006249102,"about_ca_system_score_gemma":0.001719968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00102948,"about_ca_topic_score_gemma":0.001330069,"domain_scores_codex":[0.9972029,0.0005927077,0.0001813389,0.0004443456,0.001506911,0.00007197969],"domain_scores_gemma":[0.9959334,0.001390539,0.0003195556,0.0007669346,0.001475328,0.0001143563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001323435,0.0003087903,0.003056659,0.001085031,0.0002752687,0.000442873,0.000512951,0.06455473,0.1581938,0.06490539,0.006625351,0.6999068],"study_design_scores_gemma":[0.00005547455,0.0002601661,0.001910588,0.0001051729,0.0000526415,0.0008895808,0.0001268919,0.8171615,0.09418133,0.028411,0.05668318,0.0001625234],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008180068,0.00002775166,0.9980491,0.00003278482,0.00001653642,0.00003413705,0.00005554666,0.000807968,0.0001580985],"genre_scores_gemma":[0.006032723,0.00009610989,0.9927753,0.00002837036,0.00001334353,0.0001681051,0.0001515392,0.0003402453,0.00039429],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004623723,"threshold_uncertainty_score":0.02386934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1045184450241472,"score_gpt":0.3651098636517218,"score_spread":0.2605914186275746,"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."}}