Review of Conservation Challenges and Possible Solutions for Grassland Birds of the North American Great Plains
Bibliographic record
Abstract
North America's grassland birds remain in crisis despite decades of conservation efforts. This review provides an overview of factors contributing to these declines, as well as strategies and resources available to a diversity of stakeholders to help conserve grassland bird communities with an emphasis on the Great Plains—a grassland region of global ecological significance and a habitat stronghold for grassland birds. Grassland bird declines are driven by historical and continuing threats across the full annual cycle including grassland habitat loss, agriculture intensification, woody encroachment, and disruption of fire and grazing regimes. More recently, energy development activities, the use of neonicotinoid pesticides, and anthropogenic climate change have emerged as additional threats. While threats to grassland birds are numerous and often synergistic, possibilities for conservation are also diverse and multifaceted. Land set-aside programs, incentives and voluntary practices for producers, improved environmental management by energy and utility companies, and policy and regulation can all contribute to the conservation of these unique species. We suggest that future grassland bird research should focus on poorly studied aspects of the annual cycle, such as overwinter survival and habitat use, and the migratory period, which remains completely unexplored for many species. Filling these knowledge gaps may facilitate more sophisticated population modeling that can identify limiting factors and more effectively guide investment in conservation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".