Incorporating Low Frequency Noise Legislation for the Energy Industry in Alberta, Canada
Bibliographic record
Abstract
Environmental noise from energy industry facilities in Alberta, Canada, is regulated by the province's Energy Resources Conservation Board (ERCB) (until 2008 known as the Alberta Energy and Utilities Board [EUB]) as set out in Directive 038: Noise Control. The 2007 edition of the directive, which comprises a comprehensive policy and guide, adopts A-weighted energy equivalent sound levels (LAeq), with sound pressure level criteria, as the primary measurement system for a receptor location. With the receptor being some distance from the energy industry noise source, the high and medium frequency components can dissipate or be absorbed by air and ground conditions, leaving mostly low frequency noise (LFN). Consequently, A-weighted measurements do not reflect the full annoyance potential of the remaining industrial noise. Complaints related to LFN are often described by the affected party as a deep, heavy sound, like “humming”, sometimes with an accompanying vibration. In some cases, the direction of the source of the LFN will be unknown to the receptor. However, it is the complainant that is most able to detect the presence of the LFN, signifying a particular sensitivity of the individual to the sound while others in the same family may not be able to detect the sound at all. To make a proper determination for the presence of LFN, the data must be collected during a time when environmental conditions are representative of when the sound is annoying. Residents who are impacted by LFN may suffer from sleep disturbances, headaches, and in some cases chronic fatigue. This paper examines the work undertaken by the ERCB to understand the issue, the various metrics tested to easily identify LFN, and finally the approach that would be incorporated into the new 2007 edition of Directive 038: Noise Control to address the problem.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".