{"id":"W4395662958","doi":"10.1038/s41598-024-59252-8","title":"Deep convolutional generative adversarial network for generation of computed tomography images of discontinuously carbon fiber reinforced polymer microstructures","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Deutsche Forschungsgemeinschaft; Karlsruhe Institute of Technology","keywords":"Computed tomography; Adversarial system; Generative adversarial network; Generative grammar; Computer science; Artificial intelligence; Microstructure; Fiber; Materials science; Carbon fibers; Tomography; Polymer; Deep learning; Pattern recognition (psychology); Composite material; Computer vision; Radiology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004364976,0.0001081506,0.0002452852,0.0001358376,0.00009492841,0.00004739016,0.00005443994,0.00006850329,0.00007602901],"category_scores_gemma":[0.00005453206,0.00008580203,0.0001734856,0.0004073573,0.0004529787,0.00005192422,0.00003237281,0.00008269877,3.303852e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001964296,"about_ca_system_score_gemma":0.0001892024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005347907,"about_ca_topic_score_gemma":0.000003895663,"domain_scores_codex":[0.9985698,0.00001783118,0.0005515137,0.0003887462,0.0003029094,0.0001691938],"domain_scores_gemma":[0.9990391,0.00004410898,0.0002126551,0.0003421704,0.0002858994,0.00007613988],"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":[0.00002654771,0.00002257371,0.0003449463,0.0001204472,0.00008950035,0.00001596414,0.0001442358,0.000325204,0.9017081,0.002423149,0.09360012,0.001179189],"study_design_scores_gemma":[0.0002717674,0.00008122879,0.0003617055,0.0001951536,0.000180993,0.0001185847,0.00001554251,0.07640813,0.9123434,0.001824976,0.008070895,0.0001276548],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7648266,0.004175718,0.2166946,0.001950272,0.008169557,0.00280531,0.00009900681,0.0002894478,0.0009895217],"genre_scores_gemma":[0.9413537,0.000003534,0.05353509,0.00004386932,0.0006334916,0.0000685213,0.0008818081,0.0000157568,0.00346417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1765272,"threshold_uncertainty_score":0.3498905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01432769180862297,"score_gpt":0.2786783284514216,"score_spread":0.2643506366427986,"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."}}