Climate–growth relationships at different stem heights in silver fir and Norway spruce
Bibliographic record
Abstract
We investigated the effect of climate on variations in annual ring-area increment along the stem of dominant silver fir ( Abies alba Mill.) and Norway spruce ( Picea abies (L.) Karst.) trees in the Black Forest, southwestern Germany, to test the hypothesis that growth allocation changes as a result of climate fluctuations. Stem discs were taken at three different stem heights: 1.30, 11.50, and 16.70 m. For each site and stem height, average annual ring-area increment chronologies were computed. In addition, we calculated ratios between ring-area increment of the upper stem discs and the disc at breast height to compare growth variations along the stem. Pearson correlation coefficients revealed a highly similar growth pattern at different stem heights, where the two upper discs were most similar. Bootstrapped correlation coefficients between the ring-area increment chronologies, ratios, and monthly temperature, precipitation, and self-calibrated Palmer drought sensitivity index data were calculated to analyze differences in climate response. High temperatures in early summer were found to reduce growth of high-altitude fir in the upper stem parts, whereas high temperatures in summer limit growth of high-altitude fir and spruce, especially at breast height. For low-altitude trees, high temperatures as well as low precipitation amounts during summer were found to reduce growth at all stem heights, but more strongly at breast height. Growth at breast height seems to be biased, as it over- or underestimates annual ring-area increments along the stem (and thereby volume increment), particularly during warm and dry climate conditions.
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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) | 0.000 | 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".