Comparison of impacts of oil infrastructure on grassland songbirds between landscape-scale noise addition and noise removal experiments
Bibliographic record
Abstract
As anthropogenic noise becomes increasingly widespread in natural habitats, noise addition and removal studies have become an important and commonly used method to assess the effects of noise on wildlife. Despite their wide implementation, it is difficult to determine whether the results from these studies translate to natural environments. Oil extraction operations provide a unique opportunity to conduct large-scale natural and experimental noise studies because they emit high-amplitude noise and are subject to interruptions or cessation of operations. We conducted a 6-year study aimed at identifying the effects of different types of oil infrastructure and noise on four species of grassland songbirds in the Canadian prairies. We measured abundance and nesting success of our focal species and compared between a large-scale playback experiment (i.e., noise addition) and in response to real infrastructure (i.e., noise removal). We also conducted an in-depth species analysis of reproductive output, stress, and population structure of an endangered species, Chestnut-collared Longspur ( Calcarius ornatus ). This multi-species and species-specific approach enabled us to assess noise impacts that may vary among species and for different life-history traits. Overall, our comparative study suggests that noise addition experiments can result in different conclusions regarding the ecological impacts of noise generated from in situ field studies in sites with associated disturbances. While noise clearly impacts birds in many ways, when layered with the many other ecosystem changes associated with real-world oil extraction activity, the effects of noise may be less prominent. As such, caution must be taken when applying results of noise experiments to conservation and management plans and regulations, as effects of noise predicted by lab and field noise addition experiments may simply not be realized under real-world conditions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".