Innovation in Noise Mapping in Natural Gas Compressor Stations
Bibliographic record
Abstract
Noise is generated at gas turbine-based compressor stations from a number of sources, including turbomachinery (gas turbines and compressors), airflow through inlet ducts and scrubbers, exhaust stacks, aerial coolers, and auxiliary systems. Understanding these noise sources is necessary to ensure that the working conditions on site are safe and that the audible noise at neighbouring properties is acceptable. Each noise source has different frequency content, and the overall sound pressure level (OSPL) at any location in the station yard or inside the compressor building is the result of a superposition of these noise sources. This paper presents results of multiple-point spectral noise measurements at three of TransCanada’s compressor stations on the Alberta System. A method is described to determine the overall noise map of the station yard using Delaunay Triangulation and Natural-Neighbour Interpolation techniques. The results are presented in OSPL maps, as well as animated pictures of the sound pressure level (SPL) in frequency domain which will be shown on a video at the conference. The latter will be useful in future work to determine the culprit sources and the respective dominant frequency range that contributes the most to the OSPL.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".