Local <i>was</i> best: sourcing tree seed for future climates
Bibliographic record
Abstract
As climate change accelerates, foresters are looking to ever warmer climates to secure sources of climatically adapted tree seed with which to establish healthy and productive plantations. However, as seed procurement areas approach jurisdictional boundaries (states, provinces, nations), across which seed and seed transfer systems are not typically shared, innovative approaches are required to identify those plantation areas for which suitable domestic provenances will be lacking, as well as areas in neighbouring jurisdictions with matching warmer, future climates that could fill domestic seed supply gaps. We describe a straightforward, climate envelope approach to locate these areas, using British Columbia (BC), Canada, and the Pacific Northwest (PNW), USA, to illustrate the analysis. We find that 21% of BC’s ecosystems (seed zones) will be at moderate or high risk of lacking adapted domestic provenances for plantation establishment by 2040. Importantly, however, we find large areas in the PNW that should be able to fill most of BC’s domestic seed supply gaps. Spatial analyses of this type will inform seed suppliers, managers, and policymakers where alternative seed procurement arrangements are needed and underscore the operational and policy barriers to acquiring seed from warmer jurisdictions. More broadly, they also highlight the need for interjurisdictional cooperation in matters pertaining to resource management.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".