{"id":"W4389484295","doi":"10.20944/preprints202312.0306.v1","title":"Towards Enhancing Automated Defect Detection (ADR) in Digital X-ray Radiography Applications: Synthesizing Training Data Through X-ray Intensity Distribution Modeling for Deep Learning Algorithms","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Digital radiography; Computer science; Artificial intelligence; Algorithm; Deep learning; Pipeline (software); Scalability; Throughput; Radiography; Machine learning; Computer vision; Database","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"],"consensus_categories":[],"category_scores_codex":[0.001549615,0.0007275522,0.0009249164,0.0004192273,0.0003951828,0.0001798941,0.001006653,0.0004935893,0.000006372179],"category_scores_gemma":[0.001254799,0.0009038636,0.0004098123,0.0008363642,0.00008060242,0.001199349,0.001317862,0.001863374,0.0000604971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000593277,"about_ca_system_score_gemma":0.00007976875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001376981,"about_ca_topic_score_gemma":0.00005322359,"domain_scores_codex":[0.9956914,0.00008000078,0.001204161,0.001710042,0.0003870706,0.0009273131],"domain_scores_gemma":[0.9974701,0.0003582651,0.0003146307,0.001483331,0.0002380891,0.0001356315],"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":[0.00003769487,0.00003814766,0.003329391,0.0006239815,0.0002734438,0.000007390035,0.001744348,0.9432774,0.005052503,0.00001338399,0.000001132152,0.04560117],"study_design_scores_gemma":[0.000364516,0.00001177768,0.00332104,0.00080994,0.0001468007,0.000009837874,0.00185695,0.9852689,0.005008816,0.001943556,0.0003937406,0.0008641196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1771897,0.0005380746,0.8153648,0.00001860409,0.0006312097,0.001408515,0.0002222839,0.004543168,0.00008365614],"genre_scores_gemma":[0.9816298,0.0004853836,0.0134994,0.00001001328,0.000356134,0.001324447,0.002460874,0.0002256156,0.000008335528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8044401,"threshold_uncertainty_score":0.9993412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1208902882204144,"score_gpt":0.3341033963872015,"score_spread":0.2132131081667871,"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."}}