{"id":"W2100687246","doi":"10.1109/robot.1996.509208","title":"A conical mirror pipeline inspection system","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Conic section; Conical surface; Pipeline (software); Pipeline transport; Computer science; Computer vision; Frame (networking); Process (computing); Artificial intelligence; Surface (topology); Line (geometry); Image quality; Optics; Computer graphics (images); Image (mathematics); Engineering; Physics; Mechanical engineering; Geometry; Mathematics","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":[0.0003264121,0.0003637196,0.0004654602,0.000555427,0.0004595023,0.0005246816,0.001132075,0.0007640677,0.009017067],"category_scores_gemma":[0.0003753978,0.000331447,0.0002988515,0.0003711529,0.0003641706,0.0008970402,0.0008327203,0.0004519342,0.003242927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004672354,"about_ca_system_score_gemma":0.0009946503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00163574,"about_ca_topic_score_gemma":0.001641609,"domain_scores_codex":[0.9995491,0.00003026835,0.00001594482,0.00008666146,0.0002648158,0.00005320368],"domain_scores_gemma":[0.999699,0.00002955682,0.00001935114,0.00006272874,0.0001274179,0.00006204595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005082042,0.0002383457,0.003148047,0.0001715159,0.00002974204,0.0004976669,0.0001736378,0.003003008,0.5322277,0.006700892,0.01774489,0.4355563],"study_design_scores_gemma":[0.0004043721,0.00428596,0.02131537,0.00007636401,0.0001840477,0.007822341,0.0002354123,0.2232595,0.5318617,0.003293135,0.206975,0.0002867822],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1256461,0.0005083405,0.8283488,0.0005928427,0.0002385145,0.0007153135,0.0006891976,0.01950675,0.02375424],"genre_scores_gemma":[0.5669779,0.0004270601,0.4040694,0.00043707,0.0001012329,0.0003253713,0.001066424,0.0002131404,0.0263823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009017067,"threshold_uncertainty_score":0.03016514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01700606214066016,"score_gpt":0.2186455631240867,"score_spread":0.2016395009834265,"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."}}