Female advantage? Investigating female frequency and establishment performance in high-Arctic <i>Silene acaulis</i>
Bibliographic record
Abstract
Many Arctic plants overcome limited time budgets and energy budgets by selfing, increasing the risk of inbreeding. Gynodioecious breeding systems allows for selfing by hermaphrodites, whereas enforced outcrossing through females lowers risk of inbreeding. Females persist due to female advantage in the system. Using the gynodioecious species Silene acaulis (L.) Jacq., we compared establishment performance and female frequency in 17 populations in open, sparsely vegetated habitats versus closed, denser vegetated habitats, across a regional climate gradient in high-Arctic Svalbard. For two populations, genetics were performed using microsatellites to compare inbreeding levels between habitats. As S. acaulis is a pioneer species, we expected denser vegetation to represent more competitive environments, reducing establishment performance and increasing female frequency due to female advantage. We expected similar responses to harsher regional climate. Establishment performance was reduced in closed habitats and harsher climate, but female frequency was slightly lower in closed habitats and did not differ between regional climates. High inbreeding levels may indicate that female advantage is counteracted by pollen limitation. However, female frequency increased with latitude. Lower sun angle at higher latitudes reduce the heat-trap effect of the cushion growth form, and female advantage might be expressed as response to reduced microclimate amelioration.
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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".