No stress memory pattern was detected in sugar maple and white spruce seedlings subjected to experimental droughts
Bibliographic record
Abstract
Abstract An increase in the frequency and magnitude of drought events threatens the health of forests and the economic, ecological, and societal services they provide. It has been widely demonstrated that trees undergoing a succession of stresses may accumulate lesions that in turn lead to a decrease in their vigor and eventually to death. However, recent studies have shown that a nonlethal stress should also initiate a stress memory, which triggers a faster and stronger plant defensive response when a new stress occurs. Although this mechanism is well understood in many herbaceous plants, a better understanding in trees is needed. The aim of our study was to explore the capacity of two forest tree species to develop a stress memory. A greenhouse experiment was conducted to evaluate the tree seedlings' vigor after one or two consecutive droughts separate from a rehydration period during the same growing season. No stress memory pattern was observed for the two tree species as, on the contrary, we even observed a stress accumulation pattern in sugar maple. It remains possible that some individuals in our study developed stress memory, but that we were not able to detect it. The fine‐tuning of experimental parameters and the conducting of longitudinal studies would be helpful to detect individual capacity in stress memory activation.
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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 | 0.001 |
| 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".