{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002610798,0.0004352452,0.0005307462,0.001965377,0.0002237031,0.0005811019,0.0005965218,0.0008525731,0.001383958],"category_scores_gemma":[0.001023534,0.0003054698,0.0003377915,0.0007417073,0.0004014444,0.0007545696,0.0005210671,0.0004876686,0.0006954737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002182462,"about_ca_system_score_gemma":0.0003259693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00135278,"about_ca_topic_score_gemma":0.001179755,"domain_scores_codex":[0.99945,0.00006003523,0.00002200535,0.0001423014,0.0002812551,0.00004429262],"domain_scores_gemma":[0.9994424,0.0001667299,0.00007798101,0.00008025354,0.0002094231,0.00002309786],"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":[0.0002667391,0.0001004898,0.002098966,0.0001555614,0.00003518948,0.0001050049,0.00008463571,0.01053907,0.5478373,0.001456841,0.001454041,0.4358661],"study_design_scores_gemma":[0.00004783064,0.0002178106,0.01666569,0.00003498133,0.00005021857,0.0004937708,0.0000491595,0.7209756,0.255321,0.001690251,0.004386648,0.00006699262],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09240127,0.0006086344,0.9015557,0.00009548369,0.00006376253,0.0001098419,0.0001665956,0.00242771,0.002571025],"genre_scores_gemma":[0.5057496,0.0004346081,0.4912917,0.00006517748,0.00006128049,0.0001252315,0.0003239448,0.0001511604,0.00179724],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001965377,"threshold_uncertainty_score":0.004629791,"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."}}