Seasonal decline in male‐phase duration in a protandrous plant: a response to increased mating opportunities?
Bibliographic record
Abstract
Abstract 1. We examined the effects of pollinator visitation and time of season on male‐ and female‐phase duration, using experimental manipulation and survey data from naturally occurring populations of Chamerion (= Epilobium) angustifolium (L.) J. Holub (Onagraceae). 2. Based on the observation that male mating opportunity (numbers of female flowers/numbers of male flowers) increases seasonally, we predicted that individual flowers should spend more time in the male phase early in the season when mating opportunity is low. We predicted that if seasonal changes in mating opportunity select for phase duration, male‐phase duration should decline when pollinator effects are experimentally controlled. 3. A comparison of phase duration in naturally pollinated and pollinator‐excluded plants supported this prediction: male‐phase duration in the pollinator‐exclusion treatment was longer and declined faster than in the naturally pollinated group. 4. A population survey revealed that once the effects of temperature were controlled for, male‐phase duration was negatively correlated with date, while female‐phase duration was positively correlated with date. 5. These findings suggest that seasonal variation in mating opportunity, and not just pollination rate or temperature, may play a significant role in phase duration in dichogamous plants.
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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 | 0.000 |
| 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".