{"id":"W4413392937","doi":"10.23919/acc63710.2025.11108069","title":"Integrating System Identification and Blind Source Separation for Real-Time Pipeline Monitoring: A Field Study","year":2025,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Blind signal separation; Pipeline (software); Identification (biology); Computer science; Field (mathematics); Separation (statistics); Telecommunications; Machine learning; Operating system; Mathematics","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":[0.002477355,0.0007097485,0.0005694933,0.0007127014,0.0003354812,0.0005395959,0.0007735209,0.001042476,0.0007736956],"category_scores_gemma":[0.003206597,0.0002600719,0.0003523088,0.0004425008,0.0006897805,0.001612481,0.0006314832,0.0006546117,0.0002264832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000435246,"about_ca_system_score_gemma":0.0006044795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001764264,"about_ca_topic_score_gemma":0.001257116,"domain_scores_codex":[0.9986082,0.0005477225,0.00007021161,0.000285943,0.0003871293,0.0001008173],"domain_scores_gemma":[0.9970179,0.001295151,0.0002473339,0.000354815,0.0009538733,0.0001308986],"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":[0.002266936,0.004896267,0.03059402,0.001105581,0.0002541995,0.0007159509,0.001832152,0.1184819,0.3384665,0.004179964,0.002466329,0.4947402],"study_design_scores_gemma":[0.0004532915,0.01455977,0.03765066,0.0001058344,0.0001772301,0.001093425,0.00117522,0.6588199,0.2750927,0.002748994,0.007934336,0.0001887042],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5919301,0.0002987573,0.4044001,0.0002724193,0.00007218418,0.0003725777,0.00008377601,0.0008348988,0.001735205],"genre_scores_gemma":[0.9096648,0.0001411288,0.08868822,0.00006300113,0.0000358572,0.0001127102,0.0000815177,0.00003301342,0.001179877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002477355,"threshold_uncertainty_score":0.01310164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01941350189550086,"score_gpt":0.3393494825371797,"score_spread":0.3199359806416789,"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."}}