{"id":"W2017640380","doi":"10.1115/ipc2012-90499","title":"A Combined Approach to Characterization of Dent With Metal Loss","year":2012,"lang":"en","type":"article","venue":"","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"TransCanada (Canada)","funders":"","keywords":"Characterization (materials science); Calipers; Line (geometry); Corrosion; Computer science; Forensic engineering; Materials science; Engineering; Structural engineering; Mechanical engineering; Metallurgy; Mathematics","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":[0.0000726456,0.00007561551,0.0001028546,0.00005496748,0.000008285819,0.000004982152,0.00006476758,0.00002258908,0.00001125367],"category_scores_gemma":[0.000007986904,0.00006122948,0.00001197249,0.0001447953,0.00001404312,0.0001270572,0.0000188939,0.0000370106,0.000005230956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002512952,"about_ca_system_score_gemma":0.000003013856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006291604,"about_ca_topic_score_gemma":2.696227e-7,"domain_scores_codex":[0.9996364,0.000007591269,0.00009126401,0.00005665237,0.00008290963,0.0001251497],"domain_scores_gemma":[0.9997729,0.000008379561,0.0000167541,0.0001221526,0.00002974154,0.00005004298],"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":[0.00002060287,0.0001316094,0.07083284,0.0001086024,0.00005966361,3.925215e-7,0.0004750007,0.00003755989,0.8696588,0.05828634,0.00003332127,0.0003552612],"study_design_scores_gemma":[0.0003356142,0.0001863183,0.3695578,0.0000435528,0.0000398284,0.00002703666,0.0000324733,0.001112847,0.6270593,0.001188937,0.00005411489,0.0003621964],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7492753,0.000001678206,0.235573,0.00000370124,0.00002380773,0.0001566351,0.000001764641,0.0003775473,0.01458654],"genre_scores_gemma":[0.6948617,3.048799e-7,0.3050624,0.00000677435,0.00001352912,0.00002244905,0.000007072813,0.00001497851,0.00001083876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2987249,"threshold_uncertainty_score":0.2496866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01359054165960104,"score_gpt":0.2095446339521114,"score_spread":0.1959540922925103,"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."}}