{"id":"W4384935110","doi":"10.24425/mms.2021.137134","title":"Trajectory determination for pipelines using an inspection robot and pipeline features","year":2021,"lang":"en","type":"article","venue":"Metrology and Measurement Systems","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Mitacs","keywords":"Pipeline transport; Pipeline (software); Trajectory; Computer science; Robot; Marine engineering; Artificial intelligence; Computer vision; Engineering; Mechanical engineering; Physics","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":[0.001017189,0.0001117772,0.000185916,0.0001639925,0.0002392485,0.0001109226,0.00008003131,0.000107915,5.094509e-7],"category_scores_gemma":[0.0001157634,0.000103796,0.00003022806,0.0001344296,0.0000314912,0.0003422775,0.00002747694,0.0000780667,2.003989e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005785019,"about_ca_system_score_gemma":0.00004377629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009468821,"about_ca_topic_score_gemma":0.0001635244,"domain_scores_codex":[0.9989289,0.0002087181,0.0002014044,0.0003366667,0.0001699582,0.0001543319],"domain_scores_gemma":[0.9992703,0.00002750704,0.00008989359,0.000184622,0.0003776728,0.00005004076],"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":[0.00008344158,0.0002301392,0.001246268,0.0003441233,0.00006833169,0.00002474147,0.000739885,0.0001683716,0.7518844,0.001346502,0.0004614012,0.2434024],"study_design_scores_gemma":[0.001628115,0.0008197851,0.01184555,0.00008881074,0.0001179188,0.0009406641,0.000413691,0.2274126,0.7523066,0.0008233113,0.003115341,0.0004875887],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02549629,0.003390404,0.9698061,0.0001121373,0.0006918008,0.0002454572,0.000001292587,0.0001976865,0.00005882904],"genre_scores_gemma":[0.9839953,0.00005048974,0.01549105,0.0001338134,0.000208088,0.00004632331,0.000002337379,0.000007506978,0.00006513589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.958499,"threshold_uncertainty_score":0.4232679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06035514589318156,"score_gpt":0.2879158678915461,"score_spread":0.2275607219983646,"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."}}