Wood density as a screening trait for drought sensitivity in Norway spruce
Bibliographic record
Abstract
We linked hydraulic vulnerability in Norway spruce (Picea abies (L.) Karst.) trunkwood with different cambial age to wood density and applied the findings on annual density variations in healthy and declining trees from southern Norway during 1990 to 2010. We hypothesized that drought stress due to the 2003 or 2006 European heat waves were the triggers for tree decline and focused analyses on the structure of wood that was produced after, as well as before, signs of stress, i.e., when decreases in height or diameter growth were visible. In the data set comprising previously published and new measurements, P50, i.e., the pressure potential necessary to induce a 50% loss in hydraulic conductivity, was negatively related to wood density. Declining trees produced wider annual rings with lower density than vigorous trees before their radial and height increment started to decline in 2003 or 2006. Trees that produced low-density wood under favorable water availability were more stressed by a sudden drought event because of higher P50values in their sapwood. Due to the strong genotypic relationship between wood density and growth, we suggest that spruce trees selected for fast growth might experience limited hydraulic performance under the impact of extreme heat waves.
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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 | 0.001 | 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".