Setting up light and noise maps to define the contours of a reserve of darkness and silence in Sherbrooke, Québec, Canada
Bibliographic record
Abstract
Urban sprawl brings cities closer to natural areas, making them cohabit is a significant environmental challenge. Densification can also increase light and noise pollution, negatively affecting human and biodiversity well-being. Grounded on a collaboration between a college and a university, this work combines brightness and noise maps to establish the contours of a reserve of darkness and silence in Sherbrooke's Mont-Bellevue Park (Québec, Canada). The light and sound environments of the city are mapped by the research team using two devices mounted on vehicles or bicycles: a multispectral and multidirectional brightness sensor and a sound level meter. Long-term and fixed-point light and noise measurements were also conducted to develop the mobile measurement protocol. Wind tunnel measurements in an anechoic chamber were finally used to determine the effect of travel speed on measured noise levels, showing that speeds of less than 15 km/h ensure unbiased acoustic measurements using a windscreen. Perspectives include using the Noise Capture application to provide crowdsourced and additional noise levels at fixed measurement points. The open-source Noise Modelling tool will provide us with calculated environmental noise maps to be compared with measured data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".