Adaptation to climate change: Genetic variation is both a short- and a long-term solution
Bibliographic record
Abstract
We propose a methodology combining a biophysical site index model and a seed source transfer model based on both temperature and precipitation to estimate white spruce plantation yield under present and future global warming conditions. The biophysical site index model predicts dominant height at 25 years, which is further used to estimate plantation yield using yield tables. The transfer model shows that, on average, seed sources are best adapted to the temperature conditions where they presently grow, and give maximum yield under these conditions. However, this model also shows that transfer of seed sources to drier sites could improve plantation yield. To predict site index values under climate change conditions, values obtained from the biophysical site index model are corrected by a factor estimated using the seed source transfer model. Our simulation results predict that global warming should favour a slight increase in white spruce plantation yield in southern Québec. However, one cannot expect to obtain similar yields from a seed source rapidly exposed to warmer conditions compared with a seed source that is presently growing under climatic conditions to which it has become adapted. It would take several generations (adaptation lag) for a seed source to adapt to warmer conditions. We believe that the method we propose will be helpful in identifying the most productive seed source to be used at any given location in the province, and in revising seed source transfer rules. Key words: climate change, white spruce, provenance test, transfer model, site index, adaptation, plantation, GIS
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".