{"id":"W4309785599","doi":"10.3390/s22228986","title":"A Novel Computer-Vision Approach Assisted by 2D-Wavelet Transform and Locality Sensitive Discriminant Analysis for Concrete Crack Detection","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Convolutional neural network; Locality; Robustness (evolution); Feature extraction; Wavelet; Wavelet transform; Classifier (UML); Computer vision","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":[0.0001849591,0.0001721725,0.0002775435,0.0001140874,0.0002426301,0.00003373233,0.00004856515,0.00005764142,0.000002418777],"category_scores_gemma":[0.000006295472,0.0001640459,0.0001279657,0.0002972099,0.00004083878,0.00006073476,0.00002660446,0.0001968048,2.26892e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001432673,"about_ca_system_score_gemma":0.000005533389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009807025,"about_ca_topic_score_gemma":0.00001769042,"domain_scores_codex":[0.9990759,0.00002985441,0.0002056882,0.0002659071,0.0001544646,0.0002681868],"domain_scores_gemma":[0.9996728,0.0000520526,0.00003917911,0.0001312263,0.00004402422,0.00006072469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004190987,0.00003940748,0.0001677243,0.0004042191,0.001553075,0.00002126109,0.005105154,0.478458,0.3529643,0.00009651325,0.0005505403,0.1602207],"study_design_scores_gemma":[0.0006081731,0.0001388363,0.004342597,0.000006908008,0.0002815738,0.00005270071,0.001349432,0.9694017,0.0217815,0.00001466736,0.001763924,0.000258055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.49085,0.00001609427,0.5083342,0.00002044371,0.0002101011,0.0002150074,0.0001661811,0.00007914348,0.0001089216],"genre_scores_gemma":[0.9920406,0.000006645837,0.007624143,0.00002687134,0.00009223633,0.0000320079,0.0001141162,0.00002625642,0.00003715949],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5011906,"threshold_uncertainty_score":0.6689596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01000160623203515,"score_gpt":0.216596902570368,"score_spread":0.2065952963383328,"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."}}