Relating otolith chemistry to surface water chemistry in a coastal plain estuary
Bibliographic record
Abstract
Although laboratory studies confirm that otoliths incorporate trace elements and stable isotopes from surrounding waters, few studies explore the relationship of otolith chemistry to water chemistry in the field and none include a larger suite of environmental tracers, such as rare earth elements. Using spotted seatrout (Cynoscion nebulosus) as model species, we tested the hypothesis that otoliths record the water chemistry of seagrass habitats in Chesapeake Bay. In summer 2001, we sampled water and juvenile fish in seagrass beds of the bay. Weighted linear regressions showed that [Ba/Ca]otolith and [La/Ca]otolith were best predicted by salinity and were modeled as [Ba/Ca]otolith (µmol·mol–1) = –2.25 ± 0.35 × salinity + 59.47 ± 7.01) and [La/Ca]otolith (pmol·mol–1) = –8.71 ± 0.65 × salinity + 243.87 ± 12.52. [Ba/Ca]otolith increased with [Ba/Ca]water, but the relationship was nonlinear. Salinity did not influence [Mn/Ca]otolith, but this ratio was positively correlated with [Mn/Ca]water. Although the partition coefficient of Sr (DSr = 0.23 ± 0.019) was similar to that in laboratory experiments, [Sr/Ca] in waters and otoliths was decoupled despite equal temperature exposure, suggesting that [Sr/Ca]otolith concentration may not be a simple function of water composition. However, there was a predictive relationship between [δ18O]otolith and [Sr/Ca]water ([δ18O]otolith = 1.18 ± 0.09 × [Sr/Ca]water (mmol·mol–1) – 14.286 ± 0.78) resulting from mixing between fluvial and oceanic waters. Water chemistry showed mixed values as a proxy for otolith chemistry and may not be a surrogate for otolith chemistry in wide estuaries.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".