Tarsal asymmetry, trait size, and extreme phenotypes in a sexually size-dimorphic water boatman <i>Callicorixa vulnerata</i>
Bibliographic record
Abstract
Fluctuating asymmetry (FA) in morphological traits can vary with the size of characters and the mode of selection acting on them. In a previous study, mid-leg tarsal FA (tarsal spine number and tarsal length) in the water boatman Callicorixa vulnerata was inversely related to fitness in both sexes. The mid-legs of water boatmen are used to cling to bottom substrate during underwater feeding and if under stabilizing selection, extreme phenotypes are predicted to exhibit elevated FA. In this study, it is shown that water boatmen with large or small mid-leg tarsal traits tend to have higher levels of tarsal FA than modal phenotypes, possibly because of increased homozygosity, poor genomic balance, or greater stress during development in extreme phenotypes. This relationship suggests selection against asymmetry per se will indirectly impose stabilizing selection on trait size. Regression analyses revealed that the relationship between FA and trait size differed between the sexes and was best described by a U-shaped distribution in females but by a relatively flat, negative linear association in males. These results indicate possible directional selection on male tarsal traits. Alternatively, they suggest associations between FA and trait size do not always reflect the mode of selection acting on a trait.
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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 | 0.001 | 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".