{"id":"W2597881733","doi":"10.15353/vsnl.v2i1.94","title":"Road Defect Detection in Street View Images using Texture Descriptors and Contour Maps","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Plan (archaeology); Support vector machine; Texture (cosmology); Contour line; Computer vision; Quality (philosophy); Pattern recognition (psychology); Transport engineering; Cartography; Geography; Engineering; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0003360694,0.0007525381,0.0006301092,0.004910797,0.0002126178,0.001194824,0.000672103,0.0008428567,0.001825985],"category_scores_gemma":[0.0009634467,0.0003162951,0.0006644619,0.001929575,0.0003027476,0.0009200843,0.0006336298,0.0005839546,0.00153562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003788838,"about_ca_system_score_gemma":0.0004474041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007680652,"about_ca_topic_score_gemma":0.01565608,"domain_scores_codex":[0.9996137,0.0000220964,0.00001663625,0.0001120892,0.0001517395,0.0000837686],"domain_scores_gemma":[0.9994684,0.00006898267,0.00008403413,0.0001099588,0.0002149469,0.00005379533],"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":[0.0008490788,0.0005658228,0.03047235,0.0005397855,0.0002123233,0.0009260389,0.0001978475,0.03970928,0.2204897,0.001148201,0.01890256,0.6859869],"study_design_scores_gemma":[0.00004688922,0.0002194168,0.0873021,0.00007926563,0.0001047531,0.001265568,0.0003220538,0.8104406,0.0905851,0.001409479,0.008151984,0.00007279652],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6360741,0.00152897,0.3329559,0.0002878455,0.0001426166,0.0004118977,0.009978592,0.01289064,0.00572942],"genre_scores_gemma":[0.8079824,0.0008853505,0.1739094,0.00008754068,0.00005463901,0.0000898957,0.01373789,0.0003601807,0.002892839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007680652,"threshold_uncertainty_score":0.0152719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005974248596023536,"score_gpt":0.232762559215345,"score_spread":0.2267883106193215,"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."}}