Drought-induced growth phenotypes are associated with genetic variation across a white pine hybrid zone
Bibliographic record
Abstract
Understanding relationships among warming climate, increased drought severity, and the genetic architecture of hybrid drought resilience is necessary for forest conservation and management. We calculated three drought-related tree-ring-growth indices (dendrophenotypes) using tree-ring data from hybrid Pinus strobiformis–Pinus flexilis study trees at nine sites across Colorado, New Mexico, Arizona, and Texas. Along with hybrid index (percentage of P. strobiformis ancestry inherited by a single tree) and climate variables, we used dendrophenotypes to (Q1) examine relationships among climate and hybrid index, (Q2) examine relationships between dendrophenotypes and hybrid index, and (Q3) conduct a genotype–phenotype analysis. We observed a significant correlation between hybrid index and dendrophenotypes resulting from high-temperature drought in addition to a significant correlation between those dendrophenotypes and our dataset of single-nucleotide polymorphisms. We conclude that P. strobiformis–P. flexilis trees exhibiting higher hybrid indices are more resilient to high-temperature drought events and encourage future research that identifies a genetic linkage between relevant loci and their conferred physiological benefits.
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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) | 0.001 | 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".