{"id":"W2072076513","doi":"10.1002/xrs.1208","title":"Optimal <i>K</i> <sub>α</sub> XRF detection geometry of arsenic in skin using an extended fundamental parameter method","year":2009,"lang":"en","type":"article","venue":"X-Ray Spectrometry","topic":"X-ray Spectroscopy and Fluorescence Analysis","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Allison University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Collimator; Detector; SIGNAL (programming language); Photon; Physics; Optics; Orientation (vector space); Geometry; Mathematics; Computer science","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":[0.0007977404,0.0006171692,0.0006665251,0.0003667172,0.0002298952,0.0008399003,0.0009801058,0.0006361973,0.001234266],"category_scores_gemma":[0.001501907,0.0005032831,0.0003428777,0.0003559559,0.0004627836,0.0008162094,0.0003733323,0.0003869494,0.0004132653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001093988,"about_ca_system_score_gemma":0.0009102889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001294265,"about_ca_topic_score_gemma":0.00196126,"domain_scores_codex":[0.9996257,0.0001059375,0.00001475334,0.00008127347,0.0001423247,0.00003003247],"domain_scores_gemma":[0.9993958,0.0002796696,0.00008283101,0.00008759033,0.0001347993,0.00001935312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004021924,0.0001253063,0.003046991,0.0004189031,0.00005207507,0.0002837243,0.0002344311,0.5838577,0.2586053,0.03241914,0.001427238,0.119127],"study_design_scores_gemma":[0.00003773454,0.00006265662,0.0008444318,0.00001165655,0.00001149312,0.0001117437,0.00002921338,0.948375,0.04521516,0.003773589,0.001484758,0.00004247293],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1201449,0.0003477373,0.8728268,0.0001859285,0.00002567542,0.00008713545,0.0001890844,0.001009327,0.005183369],"genre_scores_gemma":[0.3777028,0.0001835203,0.6211762,0.00002639087,0.000005857944,0.00009199511,0.0000784369,0.0001442728,0.0005904942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001294265,"threshold_uncertainty_score":0.007937431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01095809100417883,"score_gpt":0.2813516757448016,"score_spread":0.2703935847406228,"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."}}