Leaf epicuticular wax and glaucousness in Altai wildrye grass: which trait is most important to water status?
Bibliographic record
Abstract
Epicuticular wax (EW) concentration on the outermost layer of the plant cuticle increases in response to drought stress for many xeric plant species. Glaucousness, or the visible (blue) waxiness of leaf surfaces, is associated with greater EW concentration compared with nonglaucous (green) plants of Altai wildrye grass [Elymus angustus (Trin.) Pilger] (AWR). The contributions of EW concentration and glaucousness to drought stress response have been confounded in previous research. The objective of this study was to determine the effects of EW concentration and glaucousness on water relations of AWR. Water loss rate (WLR) of excised leaves was determined for 180 half-sib AWR lines in 1988, 1989 and 1990 at Swift Current, Saskatchewan, Canada. The mean WLR of blue half-sib lines was 36% less than green half-sib lines (P < 0.05). Among blue (n = 60) or among green (n = 60) half-sib lines, however, there was no significant correlation between WLR and EW concentration. Leaf water potential (Ψ) was determined for 10 blue and 10 green half-sib lines in 1988, 1989 and 1990. Predawn leaf Ψ was 18% higher for blue half-sib lines than for green half-sib lines (P < 0.01), but there was no correlation between Ψ and EW concentration within either group of half-sib lines. Four contrasting synthetics were generated by inter-crossing parent plants that had high or low EW production within either phenotype. The EW concentration, forage yield, predawn Ψ, and midday Ψ were determined for these four synthetics for three sampling dates per year from 1996 to 2001. Selection for high and low EW concentration shifted this trait (P < 0.01) by 0.19 g m-2 between glaucous synthetics and by 0.07 g m-2 between nonglaucous synthetics. Glaucous synthetics exhibited improved predawn Ψ by 0.05 MPa and predawn turgor potential (P) by 0.08 MPa (P < 0.05) compared with nonglaucous synthetics. However, selection for high and low EW concentration did not affect predawn Ψ nor predawn P in either glaucous or nonglaucous synthetics. Selection for blue glaucousness in AWR altered water relations, but selection for EW concentration did not. Key words: Forage breeding, range improvement, forage yield, drought stress, water potential, turgor potential, epicuticular wax
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".