Opportunities and challenges to implementing bird conservation on private lands
Bibliographic record
Abstract
Abstract With >70% of the United States held in private ownership, land‐use decisions of landowners will ultimately dictate the future of bird conservation in North America. However, land‐use objectives of landowners vary considerably and present opportunities and challenges for bird conservationists. Innovative strategies incorporating proactive approaches to address educational, financial, social, and economic needs of landowners are required to garner participation in conservation programs and practices to create or enhance bird habitat on privately owned working lands. Farm Bill conservation programs and practices provide unprecedented opportunities to facilitate bird conservation at regional and national scales and frequently serve as the primary vehicle for many non‐governmental organizations to accomplish their bird conservation goals. We identify current challenges and opportunities for bird conservation on private lands and present 4 case studies whereby partnerships with federal agencies, mainly the U.S. Department of Agriculture's Natural Resources Conservation Service, have proven successful in eliciting positive, measurable outcomes to bird conservation efforts on private lands spanning many North American physiographic regions. The future of bird conservation will increasingly rely upon the ability of federal agencies to prioritize and allocate additional resources to deliver bird conservation programs on private lands and a greater awareness by conservationists of the role of economics in the decision‐making process of landowners. © 2013 The Wildlife Society.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".