Impacts of oil well drilling and operating noise on abundance and productivity of grassland songbirds
Bibliographic record
Abstract
Abstract Anthropogenic noise from natural resource extraction may negatively impact many species, particularly those reliant on acoustic communication. To compare the impacts of several types of noise resulting from oil extraction operations on habitat use and productivity of grassland songbirds, we designed and implemented a novel large‐scale, spatially and temporally replicated experiment. We recreated soundscapes produced by drilling and operating oil well noise, and compared impacts of noise‐producing and quiet playback infrastructure, in twenty‐nine 64.7‐ha native prairie sites in Alberta, Canada, from 2013 to 2015. Drilling noise recordings played 24 hr/day for 10 days, twice during each breeding season, while oil well operating noise played continuously, 24 hr/day, throughout each ~90‐day breeding season. Despite the much shorter duration of drilling noise playbacks, drilling noise negatively impacted three of our four focal species, and had a much greater impact on habitat use and productivity than did well operating noise. Infrastructure also impacted Vesper Sparrows and Sprague's Pipits, even in the absence of noise. Synthesis and applications. Acute oil drilling noise had a greater negative impact on breeding migratory birds when compared to chronic oil well noise, perhaps because drilling noise is unpredictable. While this study demonstrates that noise alone can negatively impact habitat use, nesting success and nestling quality, it is also clear that effective mitigation strategies require both noise and above‐ground infrastructure management to reduce impacts on wildlife.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".