The impact of energy extraction drilling noise on three obligate grassland songbirds in the Mixed Grasslands of Alberta
Bibliographic record
Abstract
North American grassland birds face threats from grassland conversion to agriculture and activities like oil and gas extraction. Beyond habitat loss and fragmentation, oil and gas extraction generates noise pollution, that further affects these birds. This thesis focuses on these negative acoustic impacts by looking at the landscape-scale effects of drilling noise on the abundance and nest success of three species of declining grassland songbirds. All my focal species, Chestnut-collared longspur (Calcarius ornatus), Savannah sparrow (Passerculus sandwichensis), and Sprague's pipit (Anthus spragueii), experience adverse effects associated with drilling noise in my region of interest, the Mixed Grasslands ecoregion of Alberta. For this project, I created layers that contained the location of drilling activity occurring in the ecoregion over ten years surrounded by buffers of different sizes that were leveraged from a previous study that calculated the ecological footprints around drilling noise for each species. I overlapped these layers with two types of species models and estimated the number of birds displaced because of oil and gas drilling noise. To calculate the number of nests affected, I conducted a literature search for estimates of nest density in the area and extrapolated them to the total area affected by drilling noise. The number of birds and nests affected depended on the number of drilling events, as well as the differences in bird density estimated by the models. Proportional to their population in the ecoregion, Savannah sparrows had the highest number of individuals displaced. In terms of the number of nests lost, Sprague's pipit was the most affected species. My estimates can inform the energy industry and the public about the number of birds and nests that are potentially lost to drilling operations so that they can mitigate or compensate accordingly.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".