Population differences in otolith chemistry have a genetic basis in Menidia menidia
Bibliographic record
Abstract
The number of studies using otolith chemistry as a tool to reconstruct the environmental history of fishes and to detect population structure continues to rise, despite the fact that factors influencing otolith deposition are not fully understood. Many studies have examined the influence of environmental parameters on otolith composition, but none to date have tested the possible influence of intrinsic factors. Using lab broodstock populations, we examined the influence of genetics and temperature on Mg:Ca, Mn:Ca, Sr:Ca, and Ba:Ca concentrations and partition coefficients in the otoliths of juvenile Atlantic silversides, Menidia menidia . Fish from two populations, South Carolina, USA, and Nova Scotia, Canada, were reared in 15, 21, and 27 °C. We found significant (p < 0.05) differences in otolith Mg:Ca, Mn:Ca, and Ba:Ca ratios as well as in Mg, Mn, and Ba partition coefficients among populations. Such genetic influences on otolith elemental concentrations have important implications for understanding the physiological mechanisms underlying otolith deposition and enhance the utility of otolith chemistry as a marker of fish population structure in the wild.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".