Drought-tolerance indices in a tall fescue population and its polycross progenies
Bibliographic record
Abstract
Development of drought-tolerant cultivars is hampered by a lack of effective selection criteria. In this research, drought tolerance of 75 genotypes of tall fescue in three sets (25 parental, 25 early, 25 late-flowering progenies) was evaluated under no soil moisture stress and soil moisture stress in the field during 2009 and 2010. Five drought-tolerance indices were calculated: stress tolerance (TOL), mean productivity (MP), geometric mean productivity (GMP), stress susceptibility index (SSI), and stress tolerance index (STI). These calculations were based on forage yield (dry matter basis) under drought (Ys) and non-drought (Yp) conditions. Soil moisture stress caused significant reduction in forage yield. Considerable genetic variation for drought tolerance was found among genotypes. A moderately high relationship was found between Yp and Ys using regression analysis, with a clear relationship in the second year. Indices GMP and STI were found to be valuable aids in the selection of drought-tolerant, high-yielding genotypes. Plots of the first and second principal components identified drought-tolerant genotypes in each set. Results indicated that selection for drought-tolerant genotypes should be planned separately for first year (establishment stage) and second year (productive stage) in tall fescue.
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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 | 0.001 | 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".