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Record W4214936836 · doi:10.3389/ffgc.2022.786237

Current Perspective Concerning the Potential Value of Chloroplast Lipidome in Assessing Moss Response to Abiotic Stress During Boreal Forest Regeneration

2022· article· en· W4214936836 on OpenAlexafffund
Grace Callahan, Xinbiao Zhu, Raymond Thomas

Bibliographic record

VenueFrontiers in Forests and Global Change · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsNatural Resources CanadaCanadian Forest ServiceMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaUniversities Space Research Association
KeywordsLipidomeMossUnderstoryAbiotic componentEcologyEnvironmental scienceBiologyTaigaAbiotic stressLipidomicsCanopy

Abstract

fetched live from OpenAlex

Mosses play important roles in the regulation of environmental or metabolic conditions in boreal forest ecosystems. Sphagnum and feathermoss are the two main bryophytes found in boreal forest understory. Clearcut harvesting (common method of boreal forest regeneration) can expose understory vegetation to water and light stress. Water and light stress can significantly impact moss growth during boreal forest regeneration. Analysis of the membrane lipidome, photosynthetic parameters and pigments can be very effective in assessing moss response to abiotic stress following clearcut harvesting. Although lipidomics is commonly used in environmental stress assessment of plants, application to assess moss lipidome and stress response is very limited. Bryophytes may alter or remodel their membrane lipid composition to acclimate or adapt to environmental stressors. Thus, this perspective provides insights into how moss lipids may serve as useful biomarkers of moss stress response or adaptation to environmental stress during boreal forest regeneration following clearcut harvesting.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.544

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)0.0000.000

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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2022
Admission routes2
Has abstractyes

Explore more

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