Geographic and seasonal variation in cold hardiness of whitebark pine
Bibliographic record
Abstract
Artificial freeze-testing utilizing the electrolyte-leakage method was used to test the cold hardiness of 2-year-old whitebark pine (Pinus albicaulis Engelm.) seedlings growing in a common garden. Testing across all seasons was used to determine the annual pattern of cold hardiness, and more intensive sampling in the fall and spring was used to assess genetic variation in cold injury among geographic regions spanning the range of the species. Mean hardiness varied widely from –9 °C in early summer to below –70 °C in the winter. Trees from interior and northern regions were the most hardy in the fall, while trees from California were the least hardy. Geographic patterns of hardiness in the spring were reversed. Significant differences in cold injury among regions were detected on all dates except during the winter. Heritability was low to moderate for both the spring (h2 = 0.18) and the fall (h2 = 0.28), and genetic correlation was weak (rA = 0.18). Only spring cold injury was genetically correlated with date of needle flush (rA = 0.34). Mean cold injury in the fall was most closely correlated with mean temperature of the coldest month in the parental environment (r = 0.81). Whitebark pine is well adapted to the low temperatures of the harsh environments where it is found; however, regional variation indicates that moving seed for restoration purposes from areas with higher winter temperatures to colder environments may increase the chance of fall cold injury.
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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".