Screening of tall fescue genotypes for relative water content and osmotic potential under drought stress
Bibliographic record
Abstract
Abstract Background Tall fescue (Festuca arundinacea Schreb.) is an important cool‐season perennial grass. Its persistence and forage yield can be severely affected by drought stresses during the hot, dry summers of the southern USA. Methods One thousand tall fescue genotypes were evaluated in the greenhouse for high relative water content (RWC) and low cell sap osmotic potential (OP). Fifty contrasting genotypes for the two traits were identified and used in further greenhouse and field studies. These genotypes were also screened with 30% PEG8000. Root and shoot characteristics were studied in 10 genotypes. Results The genotypes differed for RWC (33.7%–97.3%, mean: 79.7%) and had an almost fivefold difference in OP (−0.5 to −2.4 MPa, mean: −1.2 MPa). Significant variation (p < 0.001) for the main effects of environment and genotypes was found for RWC and OP. Apart from the greenhouse trial, no correlation was found between RWC and OP, indicating that differences in RWC might have been due to factors other than osmotic adjustment. Genotypes with either long roots or high root weights, and high root/shoot ratios demonstrated high RWC and low OP. Conclusions Genotypes with consistently high RWC and low OP were identified and used for the development of mapping populations and transcriptome studies.
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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) | 0.001 | 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".