Comparing alternative methods of modelling cumulative effects of oil and gas footprint on boreal bird abundance
Bibliographic record
Abstract
Abstract ContextOil and gas activity is increasing in the western boreal forest of North America. To manage cumulative effects of this industry, a better quantification of footprint effects on wildlife is needed.ObjectivesWe used point-count surveys to evaluate how well dose-response (amount) and zone-of-impact (distance) models for seismic lines, pipelines, well sites, roads, and energy facilities predicted the abundance of 48 bird species. MethodsWe developed models for each species, evaluating the best functional forms for different footprint effects, then predicted how different model structures influenced estimates of regional population size.ResultsMost species exhibited at least one nonlinear response to footprint amount (79% of species) or distance (88%). Species associated with older coniferous forests decreased more often with footprint amount and proximity to footprint, while species associated with open lands and young forests increased with footprint amount and proximity. One-third of species strongly changed in abundance at a threshold distance from at least one footprint. Zone-of-impact models had better model fit than dose-response models for 29 of 48 species, but both model types produced similar population estimates.ConclusionsBoth zone-of-impact and dose-response models were useful for assessing cumulative effects on wildlife and mechanisms causing change. Although we did not find evidence for edge effects distinct from habitat loss or gain for songbirds in our study, zone-of-impact models can provide evidence of positive or negative edge effects for developing management buffers, while dose-response models provide important information on functional changes in bird habitat with oil and gas footprint.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".