Incorporating genetic variation into a model of budburst phenology of coast Douglas-fir (<i>Pseudotsuga menziesii</i> var. <i>menziesii</i>)
Bibliographic record
Abstract
Models to predict budburst and other phenological events in plants are needed to forecast how climate change may impact ecosystems and for the development of mitigation strategies. Differences among genotypes are important to predicting phenological events in species that show strong clinal variation in adaptive traits. We present a model that incorporates the effects of temperature and differences among genotypes to predict the timing of budburst of coast Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco). The main components of the model are (i) functions to calculate the accumulation of chilling units (CU) and forcing units (FU) during dormancy and (ii) a function defining the combinations of CU and FU needed for budburst (the possibility line). The possibility line was fit to data from 59 populations subjected to eight different winter environments. Differences among populations were incorporated into the possibility line using population coefficients that vary the FU required for budburst. Correlations among the population coefficients and variables describing local environments supported the hypothesis that genetic variation in budburst is largely an adaptation to summer drought. The new model can be used to test potential seed transfers as a strategy to mitigate some of the effects of climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".