{"id":"W2475033849","doi":"10.1016/j.apradiso.2016.07.004","title":"Optimization of data analysis for the in vivo neutron activation analysis of aluminum in bone","year":2016,"lang":"en","type":"article","venue":"Applied Radiation and Isotopes","topic":"Nuclear Physics and Applications","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Power Generation; Canadian Nuclear Laboratories; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Nuclear Laboratories; McMaster University","keywords":"Neutron activation analysis; Detection limit; Inverse; Human bone; Aluminium; Variance (accounting); Spectral analysis; Decomposition; Mathematics; Biological system; Computer science; Biomedical engineering; Materials science; Analytical Chemistry (journal); Chemistry; Statistics; Physics; Radiochemistry; Chromatography; Medicine","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":[0.0001543732,0.0000547955,0.0001856661,0.0002295676,0.0000284605,0.000008906448,0.0001023256,0.00001761654,0.0000627621],"category_scores_gemma":[0.000002952172,0.0000373891,0.00004551473,0.0009869945,0.00002813133,0.0001183399,0.000028171,0.00002041292,1.763501e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007887425,"about_ca_system_score_gemma":0.000009380282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002139204,"about_ca_topic_score_gemma":0.0000491192,"domain_scores_codex":[0.9994732,0.00001114651,0.0002299219,0.0001610852,0.00005933076,0.0000653574],"domain_scores_gemma":[0.9993564,0.0001688725,0.000175581,0.0002630771,0.00002400311,0.00001200662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001054151,0.0004813766,0.1591247,0.00002771222,0.002399429,1.441132e-8,0.0009232683,0.2911164,0.08506197,0.2364977,0.0003228061,0.2239391],"study_design_scores_gemma":[0.001606731,0.00002112888,0.4076131,0.00001444078,0.001379109,7.476515e-9,0.0006337293,0.5701313,0.01372449,0.001997519,0.002681815,0.0001966263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.445861,0.00004633764,0.5507293,0.00123089,0.00001160607,0.0008114214,0.0004785846,0.000007126673,0.0008236759],"genre_scores_gemma":[0.9988071,0.00003639787,0.0008662477,0.00001864584,0.00002188859,0.00006100361,0.000167228,0.000004633464,0.00001682983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5529461,"threshold_uncertainty_score":0.1524683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01621241982094163,"score_gpt":0.2603122978291099,"score_spread":0.2440998780081682,"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."}}