Climate change and tree-ring relationships of <i>Nothofagus</i> <i>menziesii</i> tree-line forests
Bibliographic record
Abstract
To assess the sensitivity of New Zealand tree lines to climate warming, we compared the tree-ring growth characteristics and temperature relationships of silver beech (Nothofagus menziesii (Hook. f.) Oerst) at two elevations, ca. 1200 m (tree line) and ca. 1100 m. Modelled relationships between climate series and tree rings indicated that the main climatic control on tree growth was current summer temperatures. Nevertheless, temperatures during earlier seasons can influence tree growth, pointing to a complex relationship between radial growth and climate at tree line. Overall, the similarity in the growthtemperature relationships for trees at both elevations indicated that high-altitude N. menziesii forests should be useful for examining the impact of climate warming on tree growth. However, the level of common growth variation was greater in the below tree line chronologies, suggesting that other factors, including natural disturbance, may affect or compete with the influence of temperature on tree-ring growth at tree line. Despite the importance of summer temperatures for tree growth at or near tree line and the reported increase in summer temperatures since 1950 in New Zealand, ring widths have not increased in recent decades. We conclude, therefore, that in these N. menziesii tree-line forests there has been no detectable tree-ring growth response to climate warming.
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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".