{"id":"W4318561956","doi":"10.1016/j.ndteint.2023.102805","title":"Ultrasonic multi-view data merging using the vector coherence factor","year":2023,"lang":"en","type":"article","venue":"NDT & E International","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Coherence (philosophical gambling strategy); Computer science; Artificial intelligence; Computer vision; Ultrasonic sensor; Pixel; Weighting; Context (archaeology); Nondestructive testing; Algorithm; Mathematics; Acoustics; Physics; Geography; Statistics","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":[0.001364782,0.001476994,0.001410858,0.004271882,0.0008899845,0.002773312,0.001316231,0.0009586863,0.005746853],"category_scores_gemma":[0.003244702,0.0009858209,0.001531839,0.003777977,0.0004614521,0.003008757,0.003212158,0.001313708,0.002623712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006331613,"about_ca_system_score_gemma":0.002332115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005958572,"about_ca_topic_score_gemma":0.008491178,"domain_scores_codex":[0.9989183,0.0001059104,0.00009435054,0.0002533613,0.0004569725,0.0001710944],"domain_scores_gemma":[0.9983763,0.0002207293,0.0001289121,0.0003391578,0.0008446177,0.00009024257],"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":[0.0005625677,0.0001429753,0.002687221,0.0003056298,0.0002960258,0.0001350341,0.0003144666,0.01017285,0.1389349,0.004836601,0.007589893,0.8340218],"study_design_scores_gemma":[0.0001015152,0.0003115869,0.008758087,0.0000809306,0.0004764087,0.0005960311,0.0005823465,0.6644106,0.2735854,0.009299186,0.04159499,0.0002029181],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008278962,0.000516267,0.9870077,0.0001181094,0.00009569379,0.00008937966,0.0002591696,0.002441901,0.001192737],"genre_scores_gemma":[0.07472527,0.0004630307,0.9202107,0.00009180191,0.0000708241,0.000125307,0.001517781,0.0006749494,0.002120349],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005958572,"threshold_uncertainty_score":0.01922512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08654677024903262,"score_gpt":0.3132189301254053,"score_spread":0.2266721598763727,"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."}}