Sex ratio variation in gynodioecious<i>Lobelia siphilitica</i>: effects of population size and geographic location
Bibliographic record
Abstract
Variation in population sex ratio can be influenced by natural selection on alternate sex phenotypes as well as nonselective mechanisms, such as genetic drift and founder effects. If natural selection contributes to variation in population sex ratio, then sex ratio should covary with resource availability or herbivory. With nonselective mechanisms, sex ratio should covary with population size. We estimated sex ratio, resource availability, herbivory and size of 53 populations of gynodioecious Lobelia siphilitica. Females were more common in populations with higher annual temperatures, lower soil moisture and lower predation on female fruits, consistent with sex-specific selection. Females were also more common in small populations, consistent with drift, inbreeding or founder effects. However, small populations occurred in areas with higher temperatures than large populations, suggesting that female frequencies in small populations could be caused by sex-specific selection. Both selective and nonselective mechanisms likely affect sex ratio variation in this gynodioecious species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".