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Record W4405499850 · doi:10.32615/ps.2024.041

Impact of exogenous rhamnolipids on plant photosynthesis and biochemical parameters under prolonged heat stress

2024· article· en· W4405499850 on OpenAlexfundno aff
M.A. BOUCHRATI, Sandra Villaume, Jean-François Guise, Ivo Feußner, Nathalie Vaillant‐Gaveau, Sandrine Dhondt‐Cordelier

Bibliographic record

VenuePhotosynthetica · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsnot available
FundersUniversité de Reims Champagne-ArdenneInstitut national de la recherche scientifique
KeywordsPhotosynthesisChlorophyll fluorescenceProlinePlant physiologyChlorophyllAbiotic componentHeat stressArabidopsis thalianaAbiotic stressPhotosystem IIBiologyYield (engineering)Carbohydrate metabolismBotanyHorticultureChemistryAgronomyEcologyBiochemistryAnimal scienceMaterials science

Abstract

fetched live from OpenAlex

High temperatures severely affect plant growth and development leading to major yield losses. These temperatures are expected to increase further due to global warming, with longer and more frequent heat waves. Rhamnolipids (RLs) are known to protect several plants against various pathogens. To date, how RLs act under abiotic stresses is unexplored. In this study, we aimed to investigate whether RLs could modify Arabidopsis thaliana physiology during prolonged heat stress. Measurement of leaf gas exchange and chlorophyll fluorescence showed that heat stress reduces photosynthetic rate through stomatal limitation and reduction of photosystem II yield. Our study reported decreased chlorophyll content and accumulation of soluble sugars and proline in response to heat stress. RLs were shown to have no detrimental effect on photosynthesis and carbohydrate metabolism in all conditions. These results extend the knowledge of plant responses to prolonged heat stress.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.258
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2024
Admission routes1
Has abstractyes

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