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Record W7133107885

Assessing Cuticular Wax Responses to Heat and Drought Stress in Canadian Bread Wheat (Triticum aestivum L.)

2023· dissertation· W7133107885 on OpenAlexafffundabout
Aswini Kuruparan

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsThe Scarborough Hospital
FundersUniversity of Toronto ScarboroughAgriculture and Agri-Food CanadaUniversity of Toronto
KeywordsWaxDrought stressTrichomeHeat stressDrought toleranceWater stressDrought resistance
DOInot available

Abstract

fetched live from OpenAlex

Cuticular waxes form a hydrophobic layer that lines plant aerial surfaces. This layer has been shown to reduce non-stomatal water loss and lower leaf temperatures under heat and drought conditions, yet very little is known about the cuticular wax responses of Canadian bread wheat varieties. A series of experiments were performed to determine the cuticular wax composition of Canadian bread wheat varieties and how they change with the application of heat and drought. The results revealed that increased β-diketone accumulation and high trichome counts on the adaxial surface were induced upon drought treatment. Additionally, older lines showed a stronger β-diketone response to drought than modern varieties. In contrast, heat stress promoted trichome production on the abaxial surface, with no significant changes in wax composition. These studies lay the foundation to understand the complex relationship between heat and drought responses and to improve these responses in new 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 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 score0.720
Threshold uncertainty score0.556

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.321
Teacher spread0.280 · 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

Citations0
Published2023
Admission routes3
Has abstractyes

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