Bibliographic record
Abstract
Genomic imprinting allows maternally and paternally derived alleles to have different patterns of expression (one allele is often silent). Kin selection provides an explanation of genomic imprinting because conflicts of interest can arise between paternally and maternally inherited alleles when they have different probabilities of being present in other individuals. Our aim here is to examine the extent to which conflicts between paternally and maternally inherited alleles could arise over the allocation of resources to male and female reproduction (sex allocation), for example, conflict over the offspring sex ratio. We examine the situations in which sex allocation is influenced by competitive or cooperative interactions between relatives: local resource competition, local mate competition, and local resource enhancement. We determine solutions for diploids and haplodiploids when either the mother or the offspring controls sex allocation. Our results suggest that the greatest conflict between paternally and maternally inherited alleles and therefore the strongest selection for genomic imprinting will occur in haplodiploid species where the offspring can control sex allocation, such as the social hymenoptera and the polyembryonic parasitoid wasps. Within the social hymenoptera, we expect especially strong selection for genomic imprinting in species subject to local resource competition, such as honeybees and army ants.
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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) | 0.003 | 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".