Fatty acids in an iteroparous fish: variable complexity, identity, and phenotypic correlates
Bibliographic record
Abstract
Ecologists often focus on summarized composition when assessing complex, multivariate phenotypes such as fatty acids. Increasing complexity in fatty acid composition may offer benefits to individuals that may not be recognized by assessing mean fatty acid identity. We quantified fatty acid identity and complexity in the egg and muscle of spawning female yellow perch (Perca flavescens (Mitchill, 1814)) exposed to three overwinter thermal regimes (4, 8, and 13 °C) to evaluate (i) thermal regulation of fatty acid complexity, (ii) fatty acid complexity and identity differences between female muscle and eggs, and (iii) relationships between fatty acid complexity and egg traits. Temperature did not alter fatty acid complexity in either tissue. Muscle contained an even distribution of 12 abundant polyunsaturated fatty acids (PUFA), while eggs exhibited a simpler phenotype of eight compounds. Fatty acid complexity in female muscle was positively correlated with potential indicators of egg quality, including size and lipid content. Preferential allocation of fatty acids to eggs by females likely contributed to a simple phenotype dominated by PUFA precursors and energy sources, while muscle represented a more complex fatty acid phenotype. Recognizing complexity could elucidate variation in reproductive condition among individuals, while identifying patterns in fatty acid allocation among 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".