Digest: Survey of field-based studies identifies trends in maintenance of sex*
Bibliographic record
Abstract
Laboratory‐based models are powerful tools to investigate biological phenomena. These studies allow for minimized impact of confounding variables and enable controlled and efficient data collection. These benefits come with one caveat: the laboratory model does not, by definition, reflect the variable and uncontrolled natural environment that wild populations inhabit. Studies using wild populations can provide key insights that may be missed in some laboratory‐based designs. In this issue, Neiman et al. () synthesize findings from 66 studies using wild populations to investigate mechanisms for maintaining sexual reproduction. The work references Graham Bell's statement that maintaining sex in nature is the “queen of problems,” due to the inherit biological limitations of sexual reproduction (Bell ). These limitations include the cost of males (who are not able to directly produce offspring and are dependent on females for their reproductive output), maintenance of expensive reproductive machinery, and time invested in locating and courting mates (reviewed by Lehtonen et al. ). Neiman et al. () investigate five key mechanisms for maintaining sex in wild populations: selection pressure by parasitism (the Red Queen hypothesis), sexual reproduction increasing adaptive evolution, reduced accumulation of deleterious mutations, improved ability to exploit differentiated niches, and improved ability to exploit broad niches. The literature survey approach allowed for a broad‐scale investigation into how each of these mechanisms functions in the wild and allowed for comparison of these results to lab‐based and theoretical hypotheses.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".