Landscape-scale effects of oil and gas development on grassland passerines in southern Alberta
Bibliographic record
Abstract
Agriculture and, more recently, oil and gas development have contributed to extensive degradation and loss of temperate grasslands. I investigated the landscape-scale effects of oil and gas development, and roads, on grassland birds in southern Alberta using abundance, clutch size and nesting success data collected from 2010-2014. I estimated: (i) the distance at which there are effects of edge, and effects of shallow gas well density, using piecewise regressions; (ii) the locations and extent of habitat affected by infrastructure for obligate grassland species– Baird’s Sparrow (Ammodramus bairdii), Chestnut-collared Longspur (Calcarius ornatus) and Sprague’s Pipit (Anthus spragueii); and generalist species – Clay-colored Sparrows (Spizella pallida), Horned Lark (Eremophila alpestris), Savannah Sparrow (Passerculus sandwichensis), Vesper Sparrow (Pooecetes gramineus) and Western Meadowlark (Sturnella neglecta), and (iii) the total area affected by wells and roads. My findings suggest that the effects of roads, overall, extended to further distances than edge effects associated with natural gas wells, obligate species had more habitat affected by infrastructure than generalist species and shallow gas wells affected more habitat than did oil wells, due to their greater density on the landscape. Additionally, obligates, on average, were negatively affected by proximity to edge where as generalists were more productivity closer to edge. Reducing fragmentation caused by roads, minimizing the spread of non-native vegetation and management of cattle around gas wells could improve habitat quality for these focal species.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".