Ontario ministry of the environment noise guidelines on wind power projects
Bibliographic record
Abstract
In response to the anticipated introduction of large scale wind power projects for electricity generation in Ontario, the Ministry of the Environment (MOE) prepared specific guidelines for assisting proponents of such project to address the current environmental regulatory requirements.This article is a brief overview of the MOE review and approval process concerning environmental noise impacts o f wind energy projects. r é s u m éEn réponse à l'introduction prévue de projets d 'éoliennes à grande échelle pour la production d 'électricité en Ontario, le ministère de l 'Environnement (MOE) a préparé des directives spécifiques pour aider les promoteurs d 'un tel projet à adresser les obligations environnementales courantes.Cet article est une brève vue du processus de revue et d 'approbation du MOE au sujet des impacts sonores dus à de tels projets.V Wired and Wireless (opt) V USB, Serial, and LAN(opt) Connectivity V Remote Display s and Programming V SPL, Leq, Thresholds, Alert and Alarm V Filters (A,C,Z), Thresholds, Calibration V Multiple Profiles (opt) V 100 dB Display Range: V 20-120 dBSPL and 40-140 dBSPL V Real-time Clock/Calendar V Internal Storage: 10+days @1/sec V Remote Storage of 1/8 second events V 7052S Type 1.5TM Titanium Measurement Mic
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".