Base Temperatures for Seedling Growth and Their Correlation with Chilling Sensitivity for Warm‐Season Grasses
Bibliographic record
Abstract
Initial screening of warm‐season grasses for cultivation in cool, short season growing areas has been focused on frost and chilling tolerance. Adoption of warm‐season grasses in these areas has resulted in an increase in degree‐day modeling of their growth. These predictive models are dependent on accurate determination of the basal temperatures for growth. In this study, base temperatures for seedling growth were estimated for switchgrass ( Panicum virgatum L.), big bluestem ( Andropogon gerardii Vitman), Indian grass ( Sorghastrum nutans L. Nash), and prairie sandreed [ Calamovilfa longifolia (Hook) Scribn.]. Seedlings at the two‐leaf stage were grown at 4, 8, 12, 16, and 24°C in growth chambers for 4 wk with representative harvests every week. Relative growth rates were calculated for each species at each temperature and these were used, in conjunction with regression techniques, to estimate base temperatures for growth. The base temperatures were then correlated with chilling sensitivity of the plants, estimated using visual scores, chlorophyll fluorescence, and electrolyte leakage. The estimated base temperatures ranged from 2.6 to 7.3°C. There were variations among and within species in base temperatures for seedling growth. There were positive correlations between base temperatures for growth and rate of electrolyte leakage ( r = 0.73), chlorophyll fluorescence (F V /F M ; r = 0.80) and leaf damage (visual score; r = 0.76). These correlations confirm the differences in adaptation of warm‐season grasses, both within and across species. They also support the differences in base temperatures. This highlights the need to use different base temperatures in statistical growth models for different species or cultivars.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".