{"id":"W3000031647","doi":"10.1061/9780784481653.064","title":"Automated Sewer Pipeline Inspection Using Computer Vision Techniques","year":2018,"lang":"en","type":"article","venue":"Pipelines 2018","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Pipeline (software); Computer science; Pipeline transport; Computer vision; Computer graphics (images); Artificial intelligence; Engineering; Operating system; Mechanical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002632921,0.000541782,0.0004577992,0.001621127,0.0002359207,0.0005732736,0.0005642922,0.0006337228,0.001482415],"category_scores_gemma":[0.0007270582,0.0002739715,0.00047415,0.0008962145,0.0002426915,0.000610882,0.0003310324,0.0004260866,0.0005611735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006488013,"about_ca_system_score_gemma":0.0008456506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009850466,"about_ca_topic_score_gemma":0.0109234,"domain_scores_codex":[0.9995663,0.00004069243,0.00001690198,0.0001214855,0.0002022212,0.00005243886],"domain_scores_gemma":[0.99966,0.00007196325,0.00006172815,0.00003793188,0.0001560614,0.00001230211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001290777,0.0002335882,0.002952119,0.0001572538,0.00006312522,0.0001444614,0.00007594846,0.113361,0.1242639,0.001077037,0.002729462,0.754813],"study_design_scores_gemma":[0.00000691121,0.00007375488,0.005348305,0.00001280883,0.00001297097,0.0001088064,0.00002008202,0.9678048,0.02466389,0.0005452212,0.001389106,0.00001333902],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0944688,0.0005287788,0.8973269,0.0001313185,0.00004347589,0.0001287404,0.0001518955,0.003729901,0.003490158],"genre_scores_gemma":[0.6654259,0.0004567106,0.3297894,0.00008873751,0.00002991413,0.00008844849,0.0003859281,0.00006171109,0.003673245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009850466,"threshold_uncertainty_score":0.01958627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009903951678761198,"score_gpt":0.2678285876777351,"score_spread":0.2579246359989739,"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."}}