Lessons learnt from multiple private land conservation programs in Canada to inform species at risk conservation
Bibliographic record
Abstract
Action at local scales is needed to reduce documented declines in global biodiversity. Agricultural land constitutes 6.8% of the surface area of Canada, including more than 20 million hectares of grazing pasture alone, providing habitat to many species at risk of extinction. In Canada, where a voluntary stewardship approach is the main strategy for species at risk conservation on privately owned and managed lands, the challenge is to create conservation programs that maximize participation. We reviewed research on voluntary conservation programs on agricultural lands from five Canadian provinces to understand which approaches were most effective in maximizing participation. The objective of this paper is to contribute to policy development for conservation on private lands in Canada. We highlight common themes from all five regions. Key findings include the value of establishing a local delivery agent for the program, and the importance of designing the program to be in alignment with existing agricultural operations. Private landowners may build trust with local program managers, and are more open to making subtle adjustments to their land management rather than major changes. We conclude with eight recommendations to support the development of high impact species at risk stewardship programs on agricultural lands in Canada.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".