{"id":"W2674352656","doi":"10.1016/j.autcon.2017.06.008","title":"Machine vision-based model for spalling detection and quantification in subway networks","year":2017,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":131,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Spall; Artificial intelligence; Computer science; Machine vision; Algorithm; Image processing; Machine learning; Engineering; Computer vision; Image (mathematics); Structural engineering","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":[0.0002099623,0.00009300315,0.0001024482,0.0001696249,0.0001573872,0.00009432976,0.00005502844,0.000104234,0.000001091485],"category_scores_gemma":[0.00005408054,0.000103122,0.00001898784,0.00005799334,0.0000371097,0.0003545536,0.000006688876,0.0001113742,4.550728e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001005631,"about_ca_system_score_gemma":0.000009840933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003960541,"about_ca_topic_score_gemma":0.0005074991,"domain_scores_codex":[0.9994181,0.000009972637,0.0002330334,0.0001435314,0.00005881192,0.0001365998],"domain_scores_gemma":[0.9996566,0.0000369438,0.00008762351,0.0001631692,0.00003708872,0.00001863626],"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":[0.00001382652,0.000002422882,0.01005325,0.00003999345,0.000002093254,1.900385e-7,0.00005875312,0.8683104,0.002920816,0.0006027435,0.000001791607,0.1179937],"study_design_scores_gemma":[0.0005055977,0.000007908352,0.05655447,0.00007511758,0.000003873994,0.000002540498,0.00002270108,0.9384199,0.002902366,0.001391685,0.00001565426,0.00009815509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3271798,0.00003198126,0.6717629,0.00002995915,0.0006420431,0.0001879368,0.000001960989,0.00008788668,0.00007547504],"genre_scores_gemma":[0.9890376,0.00003099411,0.01076294,0.000004875837,0.00007613343,0.00005840943,0.00001144916,0.00001473621,0.000002908328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6618577,"threshold_uncertainty_score":0.4205193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0106491069224853,"score_gpt":0.2508803314358804,"score_spread":0.2402312245133951,"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."}}