{"id":"W4412084016","doi":"10.1115/1.4069097","title":"A Feature-Engineering Approach to Support Vector Machine-Based Damage Detection in Lead Zirconate Titanate Ceramics Via Point-Contact Wavefield Measurement","year":2025,"lang":"en","type":"article","venue":"Journal of Nondestructive Evaluation Diagnostics and Prognostics of Engineering Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity College","funders":"","keywords":"Support vector machine; Feature (linguistics); Ceramic; Point (geometry); Materials science; Multi point; Computer science; Pattern recognition (psychology); Artificial intelligence; Acoustics; Composite material; Mathematics; Physics; Geometry","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":[0.0003996039,0.0003889665,0.0004278684,0.000875801,0.0001590018,0.0004315552,0.0005572429,0.0004876258,0.0006756567],"category_scores_gemma":[0.001146733,0.0001773561,0.0003608413,0.0005020874,0.0002946923,0.0005285288,0.0003525589,0.000439949,0.0002618693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002848532,"about_ca_system_score_gemma":0.00026788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009522616,"about_ca_topic_score_gemma":0.001047508,"domain_scores_codex":[0.9996866,0.00005790853,0.00002030428,0.00005875889,0.0001515246,0.00002498456],"domain_scores_gemma":[0.9994511,0.0001772572,0.00008115271,0.00007370431,0.0002000959,0.00001678941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001714207,0.0002514091,0.00379618,0.0001529385,0.00006450564,0.0001929575,0.0001095015,0.1607936,0.1383014,0.005245087,0.001209455,0.6897116],"study_design_scores_gemma":[0.000003226565,0.00005797295,0.0009594123,0.000003397262,0.000005001001,0.00004379163,0.00001057678,0.9850901,0.01264915,0.0007818157,0.0003892472,0.000006227795],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02659754,0.000105603,0.9724515,0.00005707137,0.00001241712,0.00002511683,0.00002524396,0.0003783693,0.0003470861],"genre_scores_gemma":[0.7267216,0.0001323103,0.271716,0.0000427167,0.00002688785,0.00007712794,0.00009474339,0.00003104741,0.001157616],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0009522616,"threshold_uncertainty_score":0.002260268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02097313469897203,"score_gpt":0.244442264409985,"score_spread":0.2234691297110129,"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."}}