Data from: Sex-specific graphs: Relating group-specific topology to demographic and landscape data
Bibliographic record
Abstract
Sex-specific genetic structure is a commonly observed pattern among vertebrate species. Facing differential selective pressures, individuals may adopt sex-specific life history traits that ultimately shape genetic variation among populations. Although differential dispersal dynamics are commonly detected in the literature, few studies have used genetic structure to investigate sex-specific functional connectivity. The recent use of graph theoretic approaches in landscape genetics has demonstrated network capacities to describe complex system behaviours where network topology represents genetic interaction among subunits. Here, we partition the overall genetic structure into sex-specific graphs, revealing different male and female dispersal dynamics of a fisher (Pekania [Martes] pennanti) metapopulation in southern Ontario. Our analyses based on network topologies supported the hypothesis of male-biased dispersal. Furthermore, we demonstrated that the effect of the landscape, identified at the population level, could be partitioned among sex-specific strata. We found that female connectivity was negatively correlated with snow depth, whereas connectivity among males was not. Our findings underscore the potential of conducting sex-specific analysis by identifying landscape elements or configuration that differentially promotes or impedes functional connectivity between sexes, revealing processes that may otherwise remain cryptic. We propose that the sex-specific graph approach would be applicable to other vagile species where differential sex-specific processes are expected to occur.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".