Survival and growth of three boreal conifer species transplanted to warm sites: implications for responses to global warming and extreme climatic events
Bibliographic record
Abstract
Understanding the responses of boreal conifers to climate change are essential for future mitigation and adaptation. In this study, 3-year-old seedlings of three Japanese boreal conifers including Sakhalin fir, Yezo spruce, and Sakhalin spruce, naturally found in Hokkaido, Japan, were transplanted in spring 2016 to a cool control and two warm (air-dried interior and humid coastal) sites. We investigated survival, height, and ecophysiological traits based on three parameters: stable carbon isotope discrimination (δ13C), specific leaf area, and leaf mass specific nitrogen concentration ( N) of seedlings during experiments. The survival rates of two spruce species were gradually significantly lower in warm sites, while that of Sakhalin fir did not significantly differ among sites. The relative growth rate (RGR) of two spruce species was significantly lower in the warm-interior site than in both cool control and warm-coastal sites in 2018, although in 2017 the RGR of two spruce species was lower in the warm sites than in cool control site. The less negative δ13C value in 2018 revealed that a vapor pressure deficit might decrease the spruces’ RGR via stoma closure in the warm-interior site. We found that Sakhalin fir would be less sensitive to climate change than two spruce species.
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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 | 0.000 | 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".