Radial Growth and Physiological Response of Coniferous Trees to Arctic Amplification
Bibliographic record
Abstract
Abstract We describe the physiological responses of boreal conifers to climate change for the past 112 years using ring‐width and carbon isotope ratio (δ13C) chronologies at six forest sites in northern Eurasia and Canada. Responses differed among regions, depending on their climatic and/or geographic characteristics. Tree radial growth decreased over the past 52 years in central eastern Siberia with the higher rate of summer temperature increase than other regions, as indicated by the negative correlation between radial growth and summer temperature, but increased in northern Europe and Canada. Changes in tree‐ring δ13C indicated that recent climatic conditions have induced stronger drought stress for trees from central eastern Siberia than for those from other regions. The observed tree growth trends were compared to those simulated using a dynamic global vegetation model. Although the modeled annual net primary production (NPP) for trees generally exhibited similar decadal variation to radial growth, simulations did not show a recent decrease in tree growth, even in central eastern Siberia. This was probably due to an overestimation of the sensitivity of modeled tree NPP to precipitation. Our results suggest that the tree NPP forecasted under the expected future increases in temperature and average precipitation might be overestimated, especially in severely dry regions such as central eastern Siberia.
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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".