The effect of operational sex ratio on fertilization success and clutch size in Japanese medaka (Oryzias latipes)
Bibliographic record
Abstract
The operational sex ratio is the number of fertilizable females to mature males in a population at a particular time.Variation in this ratio is often associated with change in behaviour during mating, including differences in male tactics.In Japanese medaka (Oryzias latipes), a species of freshwater fish found throughout Japan, there are two types of male alternative mating tactics: sneaking in which small males attempt to achieve some fertilization success by joining a spawning pair and releasing sperm, and interference where there is a disruption to a reproductive event.In addition, females adjust their clutch sizes in response to male behaviour.The operational sex ratio in this species varies across latitudes and this variation is linked to differences in mating behaviour and morphology.The objective of this work is to determine whether clutch size and proportion of fertilized eggs varies with differing operational sex ratio.To measure these responses, I collected eggs from Japanese medaka under four experimental operational sex ratios.I determined fertilization success and clutch size for females, as well as female growth rate over a 4-month period.There was no significant difference in fertilized eggs or clutch size among all four treatments.This can be attributed to fertilization assurance in higher operational sex ratios.
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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 | 0.001 |
| 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) | 0.001 | 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".