{"id":"W2093303892","doi":"10.1016/j.autcon.2013.06.011","title":"Image-based retrieval of concrete crack properties for bridge inspection","year":2013,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":401,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Visualization; Tortuosity; Bridge (graph theory); Representation (politics); Computer science; Cracking; Artificial neural network; Structural engineering; Structural health monitoring; Artificial intelligence; Computer vision; Engineering; Materials science; Geotechnical 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":[0.00008580969,0.0000892509,0.0001266551,0.0001512563,0.00003956055,0.00002944912,0.00004120745,0.00007414819,0.00002167491],"category_scores_gemma":[0.00006997053,0.0000886034,0.00003605702,0.0001634125,0.00007115482,0.0003970397,0.000004258584,0.00007480883,0.000008260147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001101809,"about_ca_system_score_gemma":0.00002397409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005346429,"about_ca_topic_score_gemma":0.000002577732,"domain_scores_codex":[0.9993813,0.00001317832,0.0002891352,0.00009617637,0.00009052704,0.0001296552],"domain_scores_gemma":[0.9996132,0.00002616775,0.00007182072,0.0000956926,0.0001756584,0.00001743005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005808945,0.000003942451,0.005808081,0.0006163958,0.00002737121,3.447597e-7,0.000480649,0.01233034,0.9570889,0.0009307195,0.0009070336,0.02174817],"study_design_scores_gemma":[0.0007541945,0.00004307986,0.04712182,0.000126457,0.000009389702,0.000008592318,0.0001912168,0.5092119,0.4415897,0.0006750593,0.0001203713,0.0001481743],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9522986,0.00002240229,0.04538076,0.00004494856,0.001095171,0.0004530922,0.000004788716,0.0003273611,0.0003729079],"genre_scores_gemma":[0.9803723,0.000004914401,0.01940913,0.000008042915,0.0001176212,0.00005764186,0.0000104697,0.00001518852,0.000004759962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5154991,"threshold_uncertainty_score":0.3613142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01002153999872207,"score_gpt":0.2130994789372893,"score_spread":0.2030779389385672,"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."}}