Assessing effects of seed source and transfer potential of white birch populations using transfer functions
Bibliographic record
Abstract
Trees have adapted to their local climates, but with changes in the climate, they may currently or in the near future occupy climates that are sub-optimal for growth and survival. The goal of current reforestation is therefore to establish a new generation of trees with growth adapted to the future climate(s). Here, we present preliminary data of a study assessing the effects of seed source and transfer potential of white birch populations. Seeds from twenty-five white birch (Betula papyrifera Marsh.) populations collected acrossCanadawere grown in the greenhouse and observed for emergence time, germination and growth. The seedlings were later planted in a common garden. After one year, the seedlings were measured for height, root-collar diameter (RCD) and survival rate and average volume per seedling calculated. Transfer functions were used to estimate the climatic distance from which populations may be transferred to the test site. There was a significant effect of population on all growth variables. Initial height was positively correlated with 1-year height and survival. Germination rate negatively correlated with emergence time. Principal component analysis showed effects of seed origin on performances of the populations in the common garden. Summer temperature was the best predictor of the transfer distance.
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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 | 0.000 |
| 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) | 0.001 | 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".