{"id":"W2916429473","doi":"10.3390/infrastructures4010010","title":"Review on Computer Aided Sewer Pipeline Defect Detection and Condition Assessment","year":2019,"lang":"en","type":"article","venue":"Infrastructures","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Serviceability (structure); Automation; Computer science; Pipeline transport; Construction engineering; Sanitary sewer; Pipeline (software); Systems engineering; Consistency (knowledge bases); Engineering; Risk analysis (engineering); Artificial intelligence; Civil 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":[0.0001083386,0.0002070815,0.0002329959,0.00008212014,0.00005713768,0.00003831813,0.00007037431,0.00008519449,0.000140803],"category_scores_gemma":[0.00001161228,0.0001707472,0.00006497981,0.00008797229,0.00002101045,0.00011965,0.0000249146,0.0003305344,0.00002890633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007945306,"about_ca_system_score_gemma":0.000008138655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007053081,"about_ca_topic_score_gemma":0.000003351207,"domain_scores_codex":[0.9992027,0.0000308441,0.0002059953,0.0002030882,0.000140864,0.0002165289],"domain_scores_gemma":[0.9996065,0.00004730883,0.00004392416,0.0002057827,0.00004293692,0.00005349521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00007648837,0.00002463015,0.009719851,0.006862085,0.0003574794,0.00002969455,0.0004217455,0.1207215,0.1345552,0.001480244,0.0940646,0.6316864],"study_design_scores_gemma":[0.003436868,0.0008861223,0.6933024,0.007127431,0.0002762055,0.0003360148,0.00009563738,0.1033328,0.05360693,0.005712446,0.1297458,0.002141369],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9543049,0.003062322,0.03099808,0.00005084773,0.004382374,0.0007599802,0.00002284107,0.0006430122,0.005775592],"genre_scores_gemma":[0.9957057,0.0006277952,0.002260021,0.00086675,0.0004566087,0.00001508139,0.00001882407,0.00003030096,0.00001895965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6835825,"threshold_uncertainty_score":0.696287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003415197945494812,"score_gpt":0.2332365708591946,"score_spread":0.2298213729136998,"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."}}