Radial growth decline of white spruce (<i>Picea glauca</i>) during hot summers without drought: preliminary results from a study site south of a boreal forest border
Bibliographic record
Abstract
If the modern climate trend continues for an additional 20–40 years, forests along the southern border of Canadian taiga may shift into new ranges of annual temperature and precipitation that are similar to the modern climate of central New York State, United States. Here we investigate the possible future response of white spruce (Picea glauca (Moench) Voss) to a warmer climate by studying trees planted 90 years ago near the southern limit of their climate tolerance in central New York, 300 km south of the boreal forest where this species is prevalent. We employed high-frequency recording dendrometers to determine radial growth phenology of six mature white spruce trees during 2013–2017. The results demonstrate significant reductions in the length of radial growth periods inversely proportional to the number of hot days with air temperature exceeding 30 °C. During years with very hot summers, the start of radial growth began about 3 days earlier than the 2013–2017 average. However, in those same years, the end of radial growth was also about 17 days earlier resulting in a shorter (70 days versus 100 days) radial growth season. Abundant (350–500 mm) summer precipitation, which resulted in soil moisture values of 20%–30%, allowed us to dismiss drought as a factor. Instead, a likely cause of reduced radial growth was mean temperature that exceeded optimal growth ranges, exacerbated by temperature extremes in ranges that lead to photoinhibition. The latter case was evident in the two warmest growing seasons (2013 and 2017), marked by extended periods of high daytime temperature (>30 °C) followed by early cessation of radial growth independent of seasonal 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".