Data from: The spatial ecology of sex ratios in a dioecious plant: relations between ramet and genet sex ratios
Bibliographic record
Abstract
1. In clonal dioecious plants, the frequency and spatial distribution of flowering ramets contains information on the underlying genet sex ratio. These measures can also provide insight on potential ecological mechanisms causing variation and bias in sex ratios among populations. 2. We used a novel likelihood-based approach and spatial clustering model to estimate the genet sex ratios from flowering ramet data collected from 32 populations of dioecious Thalictrum pubescens, a clonal species from eastern N. America that occupies moist wetland and forested environments. We investigated sex ratios of seed families, clone size, patterns of flowering and plant height to determine potential causes of sex ratio bias. 3. Flowering ramet sex ratios varied considerably among populations but were significantly male-biased. Seed families grown to flowering also exhibited the same degree of male bias. Both models predicted close correspondence between ramet and genet sex ratios. The likelihood model revealed that gender differences in ramet production could not account for biased sex ratios. The spatial clustering model indicated that ramets were significantly clustered at two spatial scales and estimated similar cluster sizes and densities for both sexes. There was no evidence for spatial segregation of the sexes. Both sexes were equally likely to flower in consecutive years and repeated bouts of flowering had no effect on ramet height. 4. Synthesis. Our analyses suggest that the widespread occurrence of male-biased sex ratios in T. pubescens is unlikely to result from sexual differences in clonal growth or habitat preferences. The bias appears to become established early in the life cycle, perhaps at the seed stage as consequence of local resource competition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".