Sex‐based differences in fatty acid composition of adult walleye
Bibliographic record
Abstract
Abstract Differences in reproductive strategies of male and female fishes are presumably accompanied by differences in nutrient allocation and predicted to lead to divergence in body composition between the sexes. We compared patterns of variation in fatty acid profiles of lipids extracted from ova, liver, muscle and visceral fat between mature male and female walleye (Sander vitreus) sampled from two wild spawning stocks. Fatty acid profiles differed significantly among body tissues in both males and females, with the strongest contrast between muscle and visceral fat. Significant differences in fatty acid composition between the sexes were found in liver, muscle and visceral fat tissues. Variation among sexes and populations was greater in liver than in the other tissues. Female livers had lower relative abundances of palmitic acid (PA, 16:0) and oleic acid (OA, 18:1(n‐9)), and higher relative abundances of arachidonic acid (ARA, 20:4(n‐6)), eicosapentaenoic acid (EPA, 20:5(n‐3)) and docosahexaenoic acid (DHA, 22:6(n‐3)) compared to male livers. In addition, female muscle had higher relative abundance of OA and lower relative abundance of DHA compared to male muscle. Our results illustrate the differential effects of reproductive demands on the biochemical composition of males and females and have implications for the analysis of fatty acid profiles in studies of wild fish populations.
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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".