{"id":"W4411682895","doi":"10.1016/j.jmbbm.2025.107116","title":"Predicting rat lumbar vertebral failure patterns as synthetic μCT images using a deep convolutional generative adversarial network","year":2025,"lang":"en","type":"article","venue":"Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Sunnybrook Health Science Centre; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Alliance de recherche numérique du Canada","keywords":"Generative adversarial network; Generative grammar; Artificial intelligence; Convolutional neural network; Computer science; Adversarial system; Deep learning; Lumbar; Pattern recognition (psychology); Medicine; Radiology","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005377566,0.000830573,0.003384358,0.0007580851,0.0002367547,0.0001585926,0.00226416,0.001109114,0.002828402],"category_scores_gemma":[0.002089508,0.0005718163,0.001364115,0.001033809,0.001072293,0.0004155181,0.0006870786,0.00130175,0.000007479421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004140097,"about_ca_system_score_gemma":0.0007240134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007836398,"about_ca_topic_score_gemma":0.00000350878,"domain_scores_codex":[0.9871384,0.001266586,0.006538249,0.0005160446,0.003464119,0.001076596],"domain_scores_gemma":[0.9935104,0.0005188244,0.003210709,0.0006835097,0.001017472,0.001059092],"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":[0.0003871244,0.001077872,0.0002681647,0.0004007486,0.0006881484,0.0003711862,0.00005580843,0.00006070626,0.9933627,0.0004006911,0.0008606073,0.002066239],"study_design_scores_gemma":[0.00498493,0.001345981,0.001034965,0.003961334,0.006023051,0.001649299,0.0002654222,0.0008539973,0.9774327,0.001545937,0.0002712173,0.000631219],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9539372,0.0003794943,0.02850272,0.0009204285,0.01521085,0.0005374399,0.0004600317,0.00004857114,0.000003272558],"genre_scores_gemma":[0.9876412,0.0002366743,0.009551744,0.000140667,0.002259382,0.00002426372,0.00003371661,0.00009515613,0.00001716345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03370404,"threshold_uncertainty_score":0.9996733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009577679217552867,"score_gpt":0.2521968607150095,"score_spread":0.2426191814974566,"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."}}