Effects of flower production and predispersal seed predation on reproduction in <i>Cirsium purpuratum</i>
Bibliographic record
Abstract
Researchers have often assumed that the reproductive success of a plant increases with flower production. However, if predispersal seed predation also increases with flower production, this may counteract the increase in reproductive success expected with such increased flower production. To investigate this, we examined the effect of flower number and predispersal seed predation on seed production in two field populations of Cirsium purpuratum (Maxim.) Matsum. At both sites, the proportion of seeds or heads preyed upon per plant increased with the number of flowers (florets or heads) on each plant, while the proportion of mature seeds per plant was independent of flower number per plant. Based on these results, we predicted that an increased level of seed predation at the population level would reduce the annual flower production of each plant. The observed pattern of flower production supported this prediction. Our results suggest that increased flower production may not always improve plant reproductive success under the influence of predispersal seed predation.Key words: plant reprodutive success, predispersal seed predation, Cirsium purpuratum, flower production, plant-animal interaction.
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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 | 0.001 |
| 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".