{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007890845,0.00005760015,0.000272395,0.0001749823,0.00005501285,0.00001589636,0.0001052515,0.00001995655,0.00001302402],"category_scores_gemma":[0.00004953307,0.00004098567,0.00006467221,0.0002432464,0.00002009753,0.0003642623,0.00001388161,0.00005362625,5.269428e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001096649,"about_ca_system_score_gemma":0.00007476951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003072805,"about_ca_topic_score_gemma":0.000007274487,"domain_scores_codex":[0.9992751,0.0000442784,0.0004198788,0.00008533925,0.0001071738,0.00006827005],"domain_scores_gemma":[0.9988902,0.0002055801,0.0006743424,0.0001219179,0.00007787136,0.00003009356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001571413,0.00008832473,0.03617531,0.00002074373,0.002793992,2.087364e-7,0.0052941,0.003997957,0.03203953,0.005685028,0.0002229549,0.9135247],"study_design_scores_gemma":[0.003030538,0.000869801,0.6410003,0.000117966,0.01275142,0.000002978095,0.008081492,0.2243322,0.09611392,0.01114878,0.002181445,0.0003691158],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4716489,0.0006252707,0.5271044,0.0005019024,0.00002066804,0.00008039125,0.000007898985,8.226267e-7,0.000009739938],"genre_scores_gemma":[0.9386514,0.00008405976,0.06108781,0.0000147412,0.0001249385,9.255053e-7,0.00002638655,0.000002131573,0.000007591955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9131556,"threshold_uncertainty_score":0.1671347,"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."}}