{"id":"W2271158160","doi":"10.1109/tim.2015.2509278","title":"Automatic Crack Detection and Measurement Based on Image Analysis","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":163,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Process (computing); Measure (data warehouse); Computer vision; Robot; Truck; Field (mathematics); Artificial intelligence; Computer science; Image processing; Machine vision; Engineering; Image (mathematics); Data mining; Mathematics; Automotive 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":[],"consensus_categories":[],"category_scores_codex":[0.000312442,0.000190179,0.0001612957,0.0003446776,0.0001631191,0.00005248697,0.00003666943,0.00005399524,0.00007952244],"category_scores_gemma":[0.000006598842,0.0001462184,0.00007148849,0.0002184085,0.00003564276,0.0001651999,4.515494e-7,0.00009308068,0.00000997443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004652514,"about_ca_system_score_gemma":0.00001739916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001170406,"about_ca_topic_score_gemma":0.0001010804,"domain_scores_codex":[0.9987238,0.00003753372,0.0002412613,0.0002196182,0.0005840551,0.0001937316],"domain_scores_gemma":[0.9995727,0.00001761887,0.00004075003,0.0001555502,0.000116507,0.00009686042],"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":[0.00005386077,0.00005162431,0.0001165134,0.0000773666,0.0003657088,0.000001213393,0.0002066779,0.01087014,0.2738886,0.000006768782,0.00002437895,0.7143372],"study_design_scores_gemma":[0.002688469,0.0003214045,0.01634214,0.0002907428,0.0006612552,0.000003912842,0.0002766269,0.1076279,0.8709962,0.00005747547,0.0002953218,0.0004385353],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2030379,0.00001512354,0.7954814,0.0001137421,0.0006200889,0.0002454365,0.000009263209,0.0001786766,0.0002983295],"genre_scores_gemma":[0.9986443,0.00005477132,0.001076541,0.00007578138,0.00002992528,0.00008842735,5.414877e-7,0.00001943841,0.00001025867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7956064,"threshold_uncertainty_score":0.5962614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0152582428402552,"score_gpt":0.2135242042316524,"score_spread":0.1982659613913972,"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."}}