{"id":"W2346191017","doi":"10.1007/s11668-016-0104-3","title":"Damage Detection in Tires Using Image-Based Strain Measurements","year":2016,"lang":"en","type":"article","venue":"Journal of Failure Analysis and Prevention","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Syncrude","keywords":"Digital image correlation; Solid mechanics; Visibility; Fault detection and isolation; Displacement (psychology); Automotive engineering; Materials science; Digital image; Computer science; Image processing; Environmental science; Engineering; Image (mathematics); Computer vision; Artificial intelligence; Composite material; Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004229138,0.00007494786,0.0001718244,0.0004196211,0.0000253518,0.00002919939,0.0000412529,0.000043904,0.00002455236],"category_scores_gemma":[0.00002110714,0.0000513833,0.0001305853,0.0002842855,0.00001050087,0.0002818166,0.000004226057,0.00007907985,3.130648e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008231324,"about_ca_system_score_gemma":0.00001181462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001014987,"about_ca_topic_score_gemma":0.0002806576,"domain_scores_codex":[0.9993615,0.00004409225,0.0002859246,0.00005942248,0.0001472559,0.0001017733],"domain_scores_gemma":[0.9996973,0.00001321129,0.0001208586,0.00005799205,0.00007846541,0.00003223072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001926977,0.000009285463,0.02797676,0.00002549906,0.0003444919,0.000007227613,0.00004774069,0.01649986,0.8727546,0.000002061255,0.0000145291,0.08229862],"study_design_scores_gemma":[0.00318924,0.0002385828,0.5683112,0.0009568112,0.002205342,0.00002962984,0.0002918342,0.03851476,0.3836769,0.001602072,0.0004966117,0.0004869608],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7188104,0.00005494259,0.2809847,0.00002394775,0.0000771985,0.00002368954,0.000001178209,0.000005469265,0.00001852857],"genre_scores_gemma":[0.994185,0.00001925727,0.005675753,0.000002943228,0.0001016806,5.528209e-7,5.81929e-7,0.000006326895,0.000007930695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5403345,"threshold_uncertainty_score":0.209535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.014537279978302,"score_gpt":0.2509376135626998,"score_spread":0.2364003335843978,"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."}}