Bibliographic record
Abstract
Gynogenetic organisms are asexual females of one species that require sperm from males of another species to initiate reproduction (but except in rare instances of ‘paternal leakage’, those sperm do not contribute to the genetic make-up of the gynogens’ offspring). Gynogenetic organisms seem to combine disadvantages of both sexual and asexual reproductive strategies (e.g., mating costs, reduced genetic diversity). We borrowed logic from the Red Queen Hypothesis (RQH) to help explain the persistence of gynogenetic species in nature, which is a paradox. The RQH is the most oft-cited explanation for the maintenance of sex. It states that evolving enemies generate a constantly changing environment, which provides the conditions that make sex advantageous. Under this scenario, asexual organisms cannot evolve fast enough to ‘keep up’ with co-evolving parasites and disease causing organisms, and ultimately show reduced fitness compared to sexual individuals. The RQH tends to view asexuality generally, ignoring important nuance in nature like gynogenetic species in mixed assemblages with closely related sexual species. We outline tests of the argument that sperm dependency prevents asexual gynogens from outcompeting sexuals in mixed species assemblages and that this further allows gynogens to escape evolving enemies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".