{"id":"W2734377202","doi":"10.1039/c7ja00148g","title":"A beam path-based method for attenuation correction of confocal micro-X-ray fluorescence imaging data","year":2017,"lang":"en","type":"article","venue":"Journal of Analytical Atomic Spectrometry","topic":"X-ray Spectroscopy and Fluorescence Analysis","field":"Physics and Astronomy","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Light Source (Canada); Regional Municipality of Waterloo; University of Waterloo","funders":"","keywords":"Attenuation; Fluorescence; Confocal; X-ray fluorescence; Optics; Materials science; X-ray; Beam (structure); Analytical Chemistry (journal); Chemistry; Physics; Chromatography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001395334,0.0002200846,0.0007093673,0.0003754705,0.0002690873,0.0001782244,0.00109932,0.00006081054,0.0002617998],"category_scores_gemma":[0.0003436694,0.00018936,0.0004785914,0.0002758905,0.0002045241,0.0005779862,0.0001142202,0.0003984735,0.00000806509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001017454,"about_ca_system_score_gemma":0.0002914871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000160608,"about_ca_topic_score_gemma":0.000002626199,"domain_scores_codex":[0.997914,0.00006535368,0.0008700974,0.0003634854,0.0004314198,0.0003556862],"domain_scores_gemma":[0.9967514,0.0003665141,0.001391491,0.000919196,0.000405538,0.0001658534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001041944,0.001294569,0.5316718,0.00009743298,0.001864176,0.00002485447,0.0001157735,0.0007503802,0.404128,0.006155225,0.01142838,0.04142747],"study_design_scores_gemma":[0.002633582,0.0003582821,0.05039913,0.0002413258,0.00157039,0.00001385086,0.0004366106,0.8131142,0.1269127,0.003304411,0.0006082807,0.00040716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1612994,0.00007656025,0.8364218,0.000989667,0.0004809498,0.0001604164,0.0001014029,0.000008515839,0.0004612474],"genre_scores_gemma":[0.9312903,0.000007936437,0.06770122,0.00004511392,0.0008047291,0.000001935627,0.00004407229,0.00002040043,0.00008425023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8123639,"threshold_uncertainty_score":0.7721875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02071593265362973,"score_gpt":0.3342645694296924,"score_spread":0.3135486367760627,"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."}}