Does seed heteromorphism have different roles in the fitness of species with contrasting life history strategies?
Bibliographic record
Abstract
Punagrass ( Achnatherum brachychaetum (Godr.) Barkworth) and flechilla grande (Nassella clarazii (Ball) Barkworth) are perennial grasses native to Argentine grasslands, with contrasting life history strategies. Both species have seed heteromorphism, but punagrass is an invasive whereas flechilla is a climax species. Experiments were conducted to determine the relative contribution of chasmogamous (CH) seeds and cleistogamous (CL) seeds to growth and reproduction of the two species. CH progeny of punagrass grew and developed fast, but progeny of the largest CL seeds of the first node allocated more resources to the production of CH seeds. Growth and seed production of CH progeny of punagrass were enhanced by increasing nutrients, displaying adaptations to nutrient-rich environments. Thus, the slow-growing CL progeny may have potential to persist as a source of CH propagules until the creation of conditions favouring the less competitive CH progeny. Since CH progeny allocated more to CL seeds, but produced fewer CH seeds under low nutrient conditions, it is suggested that competitive environments would increase the production of CL seeds. Furthermore, when nutrients were abundant, punagrass allocated more to CH seeds. Under limiting nutrients, flechilla grande produced a few large CH seeds. The production of large seeds in flechilla grande may confer superior competitive ability to its progeny, but it may be disadvantageous under heavy grazing, which favours invasive species such as punagrass with both large CL seeds and many small CH seeds of high colonizing potential.
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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".