Assessing the influence of climate on the growth of green ash trees from five Plant Hardiness Zones growing in a range-wide provenance test near the species’ northern range limit
Bibliographic record
Abstract
Green ash is threatened with extirpation from emerald ash borer (EAB) attack. Because green ash has an extensive range, its restoration could require both breeding for EAB resistance and possible adaptation to a variety of environmental gradients. We assessed the growth and climate sensitivity of green ash from five Plant Hardiness Zones (PHZs) growing in a range-wide provenance test in Vermont. Although there tended to be greater growth among trees from the warmest PHZ (7), differences were rarely statistically distinguishable. For trees from all PHZs, growth was positively correlated with precipitation in the current year and negatively correlated with precipitation the year before (a possible legacy effect). Growth was negatively associated with temperature the year of ring formation but positively associated with temperatures the year before. Growth was often positively correlated with winter snow but was negatively associated with spring or fall snow for the warmest PHZs. Climate correlations for PHZ 3 were unusual in that (1) only positive correlations were detected, (2) no legacy effects were noted, and (3) despite being from the coldest region, no correlations with snow were found. Growth increased over time for the warmest PHZs during a period of simultaneous increases in temperature and precipitation.
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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 | 0.001 |
| 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".