{"id":"W2334291935","doi":"10.4043/27026-ms","title":"Pipeline Leak and Impact Detection System - PipeLIDS - Monitoring Product Dedicated to Onshore Pipelines","year":2016,"lang":"en","type":"article","venue":"Offshore Technology Conference","topic":"Water Systems and Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cybernet Systems Corporation (Canada)","funders":"","keywords":"Pipeline transport; Noise (video); Pipeline (software); Beacon; Global Positioning System; Leak; Engineering; Leak detection; Real-time computing; Computer science; Marine engineering; Telecommunications; 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":[0.0003783442,0.0008207983,0.0006276446,0.001298195,0.0003109987,0.0004104549,0.0007658781,0.0004209922,0.007723327],"category_scores_gemma":[0.0007573048,0.0003724645,0.0002288569,0.0004060779,0.0002098179,0.0006294544,0.0006832598,0.0003052261,0.002506121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005985695,"about_ca_system_score_gemma":0.0006900688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002041587,"about_ca_topic_score_gemma":0.002006734,"domain_scores_codex":[0.9994751,0.00005881417,0.00004257765,0.0001741342,0.0002116268,0.00003784632],"domain_scores_gemma":[0.9994367,0.00008626188,0.00008721689,0.00009008205,0.0002383253,0.00006151493],"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":[0.002328427,0.0003755183,0.05256413,0.001195758,0.0001118036,0.0009849793,0.0009896494,0.01247392,0.3834707,0.001487001,0.04345967,0.5005584],"study_design_scores_gemma":[0.0004318415,0.002956473,0.1337548,0.0002546823,0.0003429979,0.002461007,0.0004941696,0.2444097,0.4598638,0.001123903,0.1536376,0.0002690756],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.438782,0.001202343,0.4439576,0.0005449373,0.0004641208,0.001064173,0.007821806,0.08585761,0.02030529],"genre_scores_gemma":[0.889856,0.0002264208,0.07817201,0.0002128209,0.00008253038,0.0003535832,0.00491065,0.000406271,0.02577975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007723327,"threshold_uncertainty_score":0.02583712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01135550131802483,"score_gpt":0.2234526043677636,"score_spread":0.2120971030497388,"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."}}