THE EVOLUTION OF SPERM-ALLOCATION STRATEGIES AND THE DEGREE OF SPERM COMPETITION
Bibliographic record
Abstract
The prevailing viewpoint in the study of sperm competition is that male sperm-allocation strategies evolve in response to the degree of sperm competition an ejaculate can expect to experience within a given mating. If males cannot assess the degree of sperm competition their ejaculate will face and/or they are unable to facultatively adjust sperm investment in response to perceived levels of competition, high sperm allocation (per mating) is predicted to evolve in the context of high sperm competition. An implicit assumption of the framework used to derive this result is that the degree of sperm competition is unaffected by changes in sperm-allocation strategies. We present theory based on an alternative perspective, in which the degree of sperm competition and the sperm-allocation strategy are coupled traits that coevolve together. Our rationale is that the pattern of sperm allocation in the population will, in part, determine the level of sperm competition by affecting the number of ejaculates per female in the population. In this setting, evolution in sperm-allocation strategies is driven by changes in underlying environmental parameters that influence both the degree of sperm competition and sperm allocation. This change in perspective leads to predictions that are qualitatively different from those of previous theory.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".