Genetic adaptation of aspen (<i>Populus tremuloides</i>) populations to spring risk environments: a novel remote sensing approach
Bibliographic record
Abstract
This study investigates geographic patterns of genetic variation in aspen (Populus tremuloides Michaux.) spring phenology with the aim of understanding adaptation of populations to climatic risk environments and the practical application of guiding seed transfer. We use a classical common garden experiment to reveal genetic differences among populations from western Canada and Minnesota, and we present a novel method to seamlessly map heat-sum requirements from remotely sensed green-up dates. Both approaches reveal similar geographic patterns: we find low heat-sum requirements in northern and high-elevation aspen populations, allowing them to take full advantage of a short growing season. High heat-sum requirements were found in populations from the central boreal plains of Saskatchewan and Alberta, and populations from Minnesota exhibit moderately low heat-sum requirements for budbreak. Analysis of corresponding climate normal data shows that late budbreak is strongly associated with the driest winter and spring environments, which suggests selection pressures for late budbreak due to both frost and drought risks in early spring. We therefore caution against long-distance seed transfer of Minnesota provenances to the boreal plains of Alberta and Saskatchewan. Although such transfers have been shown to increase tree growth in short-term field tests, this planting material may be susceptible to exceptional spring droughts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".