{"id":"W1633719757","doi":"10.1063/1.2733231","title":"Multiple Image Radiography With Diffraction Enhanced Imaging For Breast Specimen","year":2007,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Radiography; Diffraction; Feature (linguistics); Computer science; Medical imaging; Contrast (vision); Conventional radiography; Sample (material); Materials science; Computer vision; Artificial intelligence; Optics; Radiology; Physics; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008768819,0.0005503689,0.0005261683,0.001036338,0.0002112106,0.0006343159,0.0008551311,0.0009042123,0.005302005],"category_scores_gemma":[0.001609445,0.0005741086,0.0004186424,0.0009744861,0.0005617515,0.001119757,0.0009395817,0.00103827,0.001417928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004299202,"about_ca_system_score_gemma":0.0003916139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003261098,"about_ca_topic_score_gemma":0.0008554103,"domain_scores_codex":[0.999307,0.0001206193,0.00002890657,0.0001190572,0.0003864756,0.00003790587],"domain_scores_gemma":[0.9994474,0.0002096565,0.000099343,0.0001246411,0.00008692114,0.00003199035],"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":[0.0002563401,0.00009282784,0.001501933,0.0005272444,0.00003703429,0.0005658052,0.0001215922,0.001516378,0.9035965,0.006339395,0.001493351,0.08395153],"study_design_scores_gemma":[0.0000605433,0.0005457135,0.00737904,0.0001242304,0.00008027258,0.01081302,0.0001178177,0.04825046,0.8900505,0.001686706,0.04079037,0.0001012394],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1221148,0.01508501,0.8401791,0.001150075,0.0004349104,0.0002553698,0.0002294112,0.002680719,0.01787056],"genre_scores_gemma":[0.1829324,0.003236628,0.8072615,0.0002678142,0.00007797265,0.0001130624,0.0001662654,0.0001401763,0.005804117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005302005,"threshold_uncertainty_score":0.01773697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006249897625960285,"score_gpt":0.2163462992016883,"score_spread":0.210096401575728,"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."}}