{"id":"W3188474086","doi":"10.1061/9780784483602.016","title":"Smart and Automated Sewer Pipeline Defect Detection and Classification","year":2021,"lang":"en","type":"article","venue":"Pipelines 2021","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pipeline (software); Computer science; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007737789,0.0001205634,0.0001234548,0.000048534,0.00005994042,0.00005363579,0.00002429231,0.00007944336,0.00002143002],"category_scores_gemma":[0.00007747663,0.0001175484,0.00002826332,0.0001560396,0.0000203527,0.0000988627,0.00002522648,0.0001129578,0.0000131185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002558459,"about_ca_system_score_gemma":0.00001047829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007383187,"about_ca_topic_score_gemma":0.00005442704,"domain_scores_codex":[0.9994133,0.00001765344,0.0001593788,0.000179619,0.00007152047,0.0001584916],"domain_scores_gemma":[0.9996653,0.00002936676,0.00001904754,0.0001306545,0.0001044657,0.00005112622],"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":[0.00001596652,0.00001428277,0.00790979,0.0002282107,0.00005917652,0.00004049847,0.0003615341,0.001467469,0.7294238,0.00009225955,0.004233539,0.2561535],"study_design_scores_gemma":[0.0005458989,0.00001554625,0.06936152,0.00008237378,0.00006603059,0.0001619914,0.0003569374,0.805815,0.09331843,0.0001595315,0.02976714,0.0003495484],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9611801,0.003174929,0.03047992,0.0001527448,0.001756282,0.0001237806,0.000008662718,0.0009356025,0.002187965],"genre_scores_gemma":[0.9975548,0.0005425293,0.001205378,0.00003188455,0.0004183337,0.00001002459,0.00001629239,0.00002312554,0.0001975713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8043476,"threshold_uncertainty_score":0.4793484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008384120614914104,"score_gpt":0.2217338635164306,"score_spread":0.2133497429015165,"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."}}