{"id":"W4414008079","doi":"10.1109/ojim.2025.3601251","title":"Fiber-Optic Sensing Technologies for Underground Pipeline Monitoring","year":2025,"lang":"en","type":"article","venue":"IEEE Open Journal of Instrumentation and Measurement","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; National Institute for Nanotechnology; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Research Council Canada","keywords":"Pipeline (software); Optical fiber; Fiber optic sensor; Computer science; Environmental science; Remote sensing; Geology; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006137985,0.00060219,0.0004066642,0.001144373,0.0003707522,0.0007421449,0.0005674298,0.00107407,0.001940195],"category_scores_gemma":[0.0005934756,0.0002666754,0.0004173371,0.001276822,0.0006092118,0.001966288,0.0007075692,0.001331536,0.000890889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006445709,"about_ca_system_score_gemma":0.000856593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001031735,"about_ca_topic_score_gemma":0.00133715,"domain_scores_codex":[0.9993572,0.00005683802,0.00003128016,0.000103183,0.0004148242,0.00003663409],"domain_scores_gemma":[0.9996455,0.00009285314,0.00007150863,0.00002014133,0.0001525951,0.00001743114],"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":[0.00007487078,0.00008512499,0.002137464,0.006131943,0.00006710074,0.0004588194,0.0002261699,0.004195144,0.1829141,0.06749298,0.01793431,0.718282],"study_design_scores_gemma":[0.00001105369,0.0003384056,0.003260067,0.001420037,0.0001081559,0.001906965,0.000206513,0.01188401,0.1007646,0.02629207,0.8536977,0.0001105185],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.02285929,0.6439776,0.268445,0.005883723,0.002486903,0.0002309486,0.000512576,0.0007408435,0.05486319],"genre_scores_gemma":[0.1656946,0.6639095,0.1479933,0.001603588,0.00145858,0.0002083839,0.0004367005,0.00007201186,0.01862341],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001940195,"threshold_uncertainty_score":0.006490588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04716368980160174,"score_gpt":0.3012139248231023,"score_spread":0.2540502350215005,"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."}}