{"id":"W2339569074","doi":"10.3390/s16040567","title":"A Novel Method to Enhance Pipeline Trajectory Determination Using Pipeline Junctions","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Pigging; Pipeline (software); Pipeline transport; Inertial measurement unit; Extended Kalman filter; Engineering; Computer science; Marine engineering; Real-time computing; Kalman filter; Artificial intelligence; Mechanical engineering","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":[0.0001919497,0.0001604771,0.0001599011,0.0001872728,0.00006337004,0.00001025243,0.00009991899,0.00009269956,0.00002436782],"category_scores_gemma":[0.0001116217,0.0001300454,0.00004459054,0.000240447,0.00001535782,0.00009035553,0.00002006498,0.0001049349,0.00002773293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002858745,"about_ca_system_score_gemma":0.00001612381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006877603,"about_ca_topic_score_gemma":0.0000221846,"domain_scores_codex":[0.9990528,0.00003079178,0.0002694518,0.0002128035,0.0001465749,0.0002876309],"domain_scores_gemma":[0.9993957,0.0001215813,0.00003466538,0.0002450091,0.00008629963,0.0001167862],"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":[0.00001013538,0.000006352491,0.00002958931,0.00003732877,0.000003877518,0.000002017558,0.0001487085,0.004448478,0.630931,0.00001566927,0.0005644758,0.3638024],"study_design_scores_gemma":[0.000208493,0.00004365366,0.002588589,0.0002182358,0.000023148,0.00006991388,0.00003360054,0.166454,0.8198802,0.0001707532,0.009900677,0.0004086455],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.39756,0.00001850192,0.6005644,0.0001202916,0.0007502738,0.0001636797,0.00001318924,0.0006701396,0.0001394929],"genre_scores_gemma":[0.567957,0.00001006617,0.4309627,0.00004003996,0.0004451166,0.00001509967,9.399942e-7,0.00004246132,0.0005265815],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3633938,"threshold_uncertainty_score":0.5303096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03261315013742978,"score_gpt":0.3522789987608311,"score_spread":0.3196658486234013,"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."}}