Simulated drilling noise affects the space use of a large terrestrial mammal
Bibliographic record
Abstract
Wildlife is exposed to increasing anthropogenic disturbances related to shale oil and gas extraction in response to rising worldwide demands. As these disturbances increase in intensity and occurrence across the landscape, understanding their impacts is essential for management. On Anticosti Island (Québec, Canada), we equipped six white‐tailed deer Odocoileus virginianus with GPS collars taking hourly locations. We then designed a playback experiment by simulating constant drilling noise emitted by generators to which half of the collared deer were exposed for a three‐week period. Deer tolerated noise levels up to 70 dB(C). However, the number of locations recorded in areas where the noise was above 70 dB(C) was on average 73% (SE ± 18%) lower than before the disturbance, which suggests that deer experienced fine scale functional habitat loss. This loss of habitat occurred up to 200 m from the noise source. The size of home ranges and movement rates did not appear to be affected by the noise disturbance. In addition, during the experiment, deer were able to relocate in areas of their home range where food availability was similar to that of sites used before the disturbance. These results show that drilling noise can affect the habitat use of white‐tailed deer. However, future research is needed to better understand the cumulative impacts of shale mining on large mammals, as this study isolated only one of the many disturbances present near mining sites and for a limited period.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".