{"id":"W4413920071","doi":"10.1016/j.jpse.2025.100353","title":"A Comprehensive Survey on Pipeline Monitoring Technologies: Advancements, Challenges, Market Opportunities and Future Directions","year":2025,"lang":"en","type":"article","venue":"Journal of Pipeline Science and Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thales (Canada)","funders":"","keywords":"Pipeline (software); Data science; Computer science; Systems engineering; Engineering; Business","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":[0.0005461971,0.0001328814,0.0002032964,0.0005299927,0.00009432526,0.00006726033,0.0001264418,0.00005173284,0.000001237824],"category_scores_gemma":[0.00009314089,0.0001127786,0.00001860471,0.0003857926,0.00005283958,0.0003406593,0.00004143435,0.0001800294,1.546784e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000649092,"about_ca_system_score_gemma":0.00003036157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000526951,"about_ca_topic_score_gemma":0.000006903142,"domain_scores_codex":[0.999199,0.00000989443,0.000292966,0.0001167277,0.0002013096,0.0001801583],"domain_scores_gemma":[0.999437,0.00005954157,0.00005209708,0.0001049155,0.0002880469,0.00005842364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001065527,0.0001103171,0.002872633,0.001564307,0.0001887556,0.00007779968,0.001778704,0.3214368,0.00791112,0.001133387,0.01758455,0.6452351],"study_design_scores_gemma":[0.001175922,0.0002026635,0.05081908,0.001550841,0.00005120639,0.0001160794,0.006587125,0.3470036,0.003787471,0.00005452313,0.5880669,0.0005845479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.454963,0.3680191,0.1241444,0.00942362,0.02925974,0.001160852,0.00009573615,0.001765241,0.01116829],"genre_scores_gemma":[0.8790916,0.117927,0.002244035,0.00001492319,0.0004007044,0.000004963252,0.000001171483,0.00001825794,0.0002973118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6446505,"threshold_uncertainty_score":0.4598979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0303249928737021,"score_gpt":0.2453264087996901,"score_spread":0.215001415925988,"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."}}