Assessing seasonal drought stress response in Norway spruce (<i>Picea abies</i> (L.) Karst.) by monitoring stem circumference and sap flow
Bibliographic record
Abstract
Abstract Summer drought frequency is expected to increase with climate change in forested regions of Europe. To examine the physiological impacts of low soil moisture on Norway spruce [Picea abies (L.) Karst.], we conducted an irrigation experiment in a Norway spruce‐dominated forest. We monitored sap flow (Qs), stem circumference and soil water potential (Ψw), measured needle water potential (Ψl), and estimated potential evapotranspiration (PET) in control and irrigated plots. Soil water availability influenced the response of Qs to PET and the impact of Qs on maximum daily stem shrinkage (MDS). The positive relationship between Qs and PET was constrained below a threshold Ψw near −0.3 MPa. MDS was higher beyond this threshold, for a given value of Qs. Higher MDS and lower tree water status (ΔW) were observed at low Ψw in control plants, suggesting the lower water potential of stems' conducting tissues. Stem circumference increase (SCI) was 62% lower in control trees following the irrigation treatment. Slight SCI recovery was observed in these trees in response to early autumn rainfall, which caused ΔW to return to its predrought state. The results demonstrate that low water availability not only reduced Qs, ΔW, SCI, Ψl and increased MDS but also altered their mutual relations. Copyright © 2014 John Wiley & Sons, Ltd.
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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".