Conserving the diversity of Ontario tree species under multiple uncertain climatic futures
Bibliographic record
Abstract
In an environment of uncertain climatic change, there is an urgent need to develop robust reserve networks, i.e., networks that conserve species across multiple climatic futures. Using climatic envelopes based on multiple future climatic scenarios, we first estimate the impacts that these scenarios might have on the distributions of 63 native tree species in the Province of Ontario. Next, an optimization model is applied to expand an existing system of reserves in this province by determining the minimum number of optimal locations for additional reserves both with and without the need for migration. The optimization model formulated differs from previous work in that it meets the need for present and future distributions to occur in the same reserve and accommodates multiple differing climate change scenarios rather than a single scenario. The reserve selection approach described here can be applied anywhere that species’ distributions and environmental grids are available. Although this approach is designed to produce a robust solution to the uncertainties over future climatic scenarios, it does not eliminate these uncertainties because the proposed solutions inherit the uncertainties of the global climate change models.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".