{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009290553,0.0001121457,0.0001496254,0.0001967712,0.0001863268,0.0004566656,0.0003013439,0.00007052424,0.000001233726],"category_scores_gemma":[0.0001298191,0.0001024339,0.00003267876,0.0003349845,0.000009048566,0.0004217765,0.000109451,0.00007943095,0.000005479526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004550275,"about_ca_system_score_gemma":0.00004525483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008724517,"about_ca_topic_score_gemma":0.0000157981,"domain_scores_codex":[0.9988812,0.0001015712,0.0003913662,0.0003653564,0.0001430441,0.0001174708],"domain_scores_gemma":[0.9989415,0.0002939985,0.0001265924,0.0003912689,0.0002147313,0.00003194255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002688612,0.001084264,0.01393007,0.0004767101,0.0001947182,0.000003817719,0.02829554,0.0004318044,0.1160537,0.5563151,0.02764514,0.2553003],"study_design_scores_gemma":[0.001270146,0.0004440846,0.001840379,0.0001492222,0.00004387271,0.000004482686,0.004386483,0.8460024,0.1425261,0.001730189,0.001280824,0.0003218495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06701048,0.0000145027,0.9271354,0.0007186305,0.0001350973,0.000999319,4.726607e-7,0.000668887,0.003317181],"genre_scores_gemma":[0.9400874,0.000003388574,0.05237367,0.00006506481,0.0000515431,0.0002128388,0.000003273486,0.000005958726,0.007196908],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8747618,"threshold_uncertainty_score":0.4403636,"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."}}