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Record W4313325258 · doi:10.1002/ecs2.4332

No stress memory pattern was detected in sugar maple and white spruce seedlings subjected to experimental droughts

2022· article· en· W4313325258 on OpenAlexaff
Zoé Ribeyre, Christian Messier, Philippe Nolet

Bibliographic record

VenueEcosphere · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Stress Responses and Tolerance
Canadian institutionsUniversité du Québec à MontréalUniversité du Québec en Outaouais
Fundersnot available
KeywordsMapleSugarHerbaceous plantStress (linguistics)Ecological successionTree (set theory)Drought stressBiologyBotanyMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.007
GPT teacher head0.195
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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