{"id":"W196424518","doi":"10.5006/c2004-04739","title":"Application of Fiber Optic Sensors to Monitor Pipeline Corrosion","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Measurement and Detection Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Pipeline (software); Corrosion; Optical fiber; Fiber optic sensor; Materials science; Corrosion monitoring; Optoelectronics; Computer science; Metallurgy; Engineering; Mechanical engineering; Telecommunications","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":[0.00007190173,0.0000571592,0.00007163858,0.00004964249,0.00001386653,0.000002578135,0.00003073592,0.00002855523,0.00003453747],"category_scores_gemma":[0.00001770051,0.00005341754,0.00002070442,0.0001462798,0.000004876687,0.00003869871,0.000005562394,0.00003689914,0.0001135924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003936473,"about_ca_system_score_gemma":0.000001811309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005426306,"about_ca_topic_score_gemma":0.000001507576,"domain_scores_codex":[0.9996362,0.000004366851,0.0001225353,0.0000710702,0.00009136826,0.00007449175],"domain_scores_gemma":[0.9997771,0.0000112828,0.0000118923,0.0001137271,0.00004217614,0.00004385221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006231027,0.00000675559,0.00001055764,0.00001203569,9.439958e-7,1.043125e-7,0.00002245759,0.5778321,0.4020036,0.00009294551,0.00005342735,0.01995889],"study_design_scores_gemma":[0.0002408757,0.00002819255,0.000212173,0.00001073166,0.000005767902,0.000001360167,0.00002342667,0.01916451,0.9765966,0.0002606136,0.003365955,0.00008978024],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1172462,0.00002266348,0.879999,0.00001912057,0.0001280952,0.0001260035,6.883709e-7,0.0001656344,0.00229262],"genre_scores_gemma":[0.7329811,0.000003464188,0.2664583,0.00001004954,0.00005101092,0.00001225434,9.918299e-7,0.00001108926,0.0004717792],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6157349,"threshold_uncertainty_score":0.2178304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01674620550419108,"score_gpt":0.2767628098108522,"score_spread":0.2600166043066611,"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."}}