{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001146739,0.0002016501,0.0001755461,0.00014773,0.0001298588,0.00005279199,0.0001185995,0.0001315233,0.00003626605],"category_scores_gemma":[0.0000123109,0.0001779367,0.00004891624,0.0002274136,0.00008060908,0.0002307004,0.00005116517,0.0001319567,0.0001057214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001077008,"about_ca_system_score_gemma":0.00001351234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003556431,"about_ca_topic_score_gemma":0.00001231864,"domain_scores_codex":[0.9990749,0.00001314643,0.0002758665,0.0001976276,0.000138323,0.0003001714],"domain_scores_gemma":[0.9994449,0.00001069732,0.00004181359,0.000238534,0.0002093175,0.00005476274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007313126,0.00005886668,0.0009257244,0.0001396406,0.00006358946,0.00002511002,0.0007015555,0.009597438,0.2960914,0.00009561161,0.5660465,0.1261814],"study_design_scores_gemma":[0.0001551259,0.00009091455,0.0005095427,0.0001104512,0.0000140413,0.00004091811,0.00001874796,0.8805447,0.08639033,0.00009361839,0.03179064,0.0002409962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2601374,0.0001089244,0.7138203,0.00004889927,0.007357968,0.000275799,0.000007073776,0.01379674,0.004446958],"genre_scores_gemma":[0.9095073,0.00002400695,0.08232559,0.000102974,0.007889358,0.000004795314,0.000009962591,0.00006080384,0.00007520826],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8709472,"threshold_uncertainty_score":0.7256048,"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."}}