Germination in Three Switchgrass Populations after Two Cycles of Divergent Selection for Seed Weight
Bibliographic record
Abstract
Switchgrass (Panicum virgatum L.) is an emerging bioenergy crop in the US, but little has been done by breeders to improve its poor seed germination and slow and inconsistent establishment. The objective of this study was to evaluate the effects of divergent selection for seed weight on germination in three switchgrass populations over two cycles with and without a cold stratification treatment. Seed from switchgrass populations 9064202, ‘Carthage’, and ‘Timber’ was sorted into light and heavy weight classes via a gravity deck and germinated in a growth chamber. Seedlings were planted to the field in isolated crossing blocks. Seed from these blocks was sorted and germinated following a divergent selection scheme for a second cycle. Seed was harvested from all derived crossing blocks representing all cycles of selection and subjected to a cold or no stratification treatment and germinated in a growth chamber. One cycle of selection for heavy seed resulted in an increase in progeny seed weight in 9064202 and Carthage but not Timber. A second cycle of selection gave unexpected results, probably due to the effects of genetic drift. The effect of seed weight was not significant for germination percentage or germination rate index. Dormancy had a larger impact on overall germination and germination rate than seed weight in the populations tested. Therefore, selection efforts focused on reducing dormancy rather than increasing seed weight are likely to be more successful in improving overall germination and germination rate in switchgrass population 9064202 and cultivars Carthage and Timber.
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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 | 0.001 |
| 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".