Can heat stress and water deficit affect cotton fiber wax content in field-grown plants?
Bibliographic record
Abstract
Climate modelling predicts a warmer climate for cotton growing regions in the future. This will impose stresses on cotton plants that may impact on cotton fiber cuticle wax levels, and in turn may make cotton textile products more challenging to scour and dye. While previous research has measured the effect of abiotic stress on cuticle wax in cotton leaves and bracts, no research is known to have been undertaken specifically measuring the impact of abiotic stress on cotton fiber wax content. Five Upland (Gossypium hirsutum L.) cotton genotypes with known different tolerances to abiotic stress, were subjected over two growing seasons, to heat stress and or water deficit during the period when fiber cuticle wax deposition occurred. Total ethanol soluble wax content was determined in mature fiber using a standard protocol. Across all genotypes and treatments, fiber wax content varied between 0.2 and 1.6 %. For Sicot 71, a standard Australian commercial genotype, and for Siokra L23 and CIM-448 two genotypes known to be tolerant to abiotic stress, fiber wax content was either unaffected or decreased following the application of abiotic stress. For CS 50, a genotype with a poor tolerance to water deficit stress, fiber wax content increased following abiotic stress. For Sicala V-2, a genotype with less tolerance to abiotic stress, fiber wax content decreased following either heat stress or water deficit treatments alone, while wax content markedly increased following the combined application of both stress treatments. The genotypic variations observed in fiber wax content and the differences in the direction of the response to stresses suggest that conventional breeding could be used to generate new genotypes with acceptable fiber wax levels adapted to future extreme climates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".