{"id":"W4388828384","doi":"10.1007/978-3-031-34910-2_10","title":"Lightweight Internal Damage Segmentation Using Thermography with and Without Attention-Based Generative Adversarial Network","year":2023,"lang":"en","type":"book-chapter","venue":"Conference proceedings of the Society for Experimental Mechanics","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Segmentation; Thermography; Artificial intelligence; Computer science; Intersection (aeronautics); Adversarial system; Generative adversarial network; Damages; Pattern recognition (psychology); Computer vision; Deep learning; Machine learning; Engineering; Transport engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009521483,0.000358547,0.0003191994,0.00003771222,0.0001995432,0.00007945727,0.000219189,0.0002212613,0.000008646333],"category_scores_gemma":[0.000002579211,0.0002718574,0.00039276,0.00005693769,0.00009619523,0.0001374048,0.00009159745,0.0002545581,2.9316e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001321843,"about_ca_system_score_gemma":0.00003893241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003165402,"about_ca_topic_score_gemma":0.000001232067,"domain_scores_codex":[0.9989844,0.000001523297,0.0002511967,0.000275685,0.0002403959,0.0002467743],"domain_scores_gemma":[0.9994038,0.00001357614,0.0002455717,0.00008660007,0.0002035454,0.00004686149],"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":[0.0002894558,0.00002469738,0.0005345126,0.0009796872,0.002286511,5.441835e-7,0.006313746,0.002762924,0.8307961,0.1540821,0.001419875,0.0005097546],"study_design_scores_gemma":[0.003482264,0.0006899164,0.00004527849,0.004666739,0.001104989,0.000008880714,0.006966768,0.3773229,0.5755034,0.02812731,0.0005645155,0.001517087],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7293277,0.001994594,0.2217689,0.000166748,0.01333284,0.01009077,0.0006690581,0.001370814,0.0212785],"genre_scores_gemma":[0.9671409,0.00009030211,0.02899818,0.00005291328,0.0007836585,0.00011047,0.00003418519,0.0002130731,0.002576284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3745599,"threshold_uncertainty_score":0.9999734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01619560839817881,"score_gpt":0.2287193229816083,"score_spread":0.2125237145834295,"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."}}