MétaCan
Menu
Back to cohort

Presentation_1_β-diketone accumulation in response to drought stress is weakened in modern bread wheat varieties (Triticum aestivum L.).pptx

2024· article· en· W6964894139 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Ecology and Soil Science
Canadian institutionsnot available
Fundersnot available
KeywordsWaxDrought stressDrought toleranceWater stressCropDrought resistance

Abstract

fetched live from OpenAlex

Cuticular waxes coating leaf surfaces can help plants tolerate drought events by reducing non-stomatal water loss. Despite their role in drought tolerance, little is known about how cuticular wax composition has changed during breeding in Canadian bread wheat (Triticum aestivum L.) varieties. To fill in this gap, flag leaves of the Canadian Heritage Bread Wheat Panel, which include 30 varieties released between 1842 and 2018, were surveyed to determine if and how cuticular wax composition in wheat has changed at two breeding ecozones over this period. Following this, a subset of varieties was subjected to drought conditions to compare their responses. As expected, modern varieties outperformed old varieties with a significantly larger head length and reaching maturity earlier. Yet, when challenged with drought, old varieties were able to significantly increase the accumulation of β-diketones to a higher extent than modern varieties. Furthermore, RNAseq was performed on the flag leaf of four modern varieties to identify potential markers that could be used for selection of higher accumulation of cuticular waxes. This analysis revealed that the W1 locus is a good candidate for selecting higher accumulation of β-diketones. These findings indicate that the variation in cuticular waxes upon drought could be further incorporated in breeding of future bread wheat varieties.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0450.003

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 head0.038
GPT teacher head0.306
Teacher spread0.268 · 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; both teacher heads agree on what is shown here.

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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueFigshareSame topicPlant Ecology and Soil ScienceFrench-language works237,207