{"id":"W2040501401","doi":"10.1016/j.nimb.2014.11.035","title":"Quantitative reconstruction of PIXE-tomography data for thin samples using GUPIX X-ray emission yields","year":2014,"lang":"en","type":"article","venue":"Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms","topic":"X-ray Spectroscopy and Fluorescence Analysis","field":"Physics and Astronomy","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Agence Nationale de la Recherche","keywords":"Software; Voxel; Computer science; Tomography; Physics; Optics; Artificial intelligence","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":[0.001442295,0.0001573496,0.0003382103,0.0002585497,0.0004133001,0.000174836,0.0001464891,0.0000544312,0.000106513],"category_scores_gemma":[0.00005647476,0.0001282826,0.00004235361,0.000405156,0.0002479142,0.0006899268,0.0001349304,0.0002475241,4.691253e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002608611,"about_ca_system_score_gemma":0.00003172992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001110524,"about_ca_topic_score_gemma":0.00001427013,"domain_scores_codex":[0.9983635,0.0004151366,0.0003543359,0.0004337794,0.0001827329,0.0002505222],"domain_scores_gemma":[0.9989148,0.0003017207,0.0002069544,0.0003231874,0.0001803073,0.0000729829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005425313,0.0002364832,0.007535776,0.0001189073,0.0002382353,1.215696e-7,0.0006190232,0.00009208871,0.8725863,0.01655016,0.00004343098,0.101437],"study_design_scores_gemma":[0.004477508,0.002438328,0.01013257,0.0022776,0.0004695317,0.00002604407,0.02651268,0.2295381,0.6208644,0.09247195,0.009480544,0.001310693],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9066551,0.000009515581,0.09236372,0.00003181767,0.0002768726,0.0002856397,0.0001321923,0.0000113774,0.000233763],"genre_scores_gemma":[0.8380845,0.00004377544,0.1615015,0.000007425065,0.0002418105,0.00002000809,0.00005937631,0.00002128851,0.00002036298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2517219,"threshold_uncertainty_score":0.5231213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1029186383000962,"score_gpt":0.4439680994314151,"score_spread":0.341049461131319,"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."}}