Evaluating otolith Sr/Ca as a tool for reconstructing estuarine habitat use
Bibliographic record
Abstract
There is no standard method to determine the applicability of otolith Sr/Ca ratio to reconstructing estuary use. We have developed a novel method to determine the response of otolith Sr/Ca to changes in water Sr/Ca and salinity in San Francisco Estuary (California, USA). We perform correlated, spatially resolved Sr/Ca and Sr isotope measurements using otoliths from adult striped bass ( Morone saxatilis ) in the San Francisco Estuary to estimate the otolith–water Sr/Ca partition coefficient (DSr = 0.305 ± 0.009). DSr did not vary significantly with salinity, and therefore the salinity–otolith Sr/Ca model was constructed by substituting the partition coefficient into the nonlinear salinity–water Sr/Ca mixing model for the system. The model demonstrates that the primary factor controlling the response of Sr/Ca to salinity is the Ca concentration in the freshwater source flowing into the estuary. A concentration of 60 ppm Ca is an approximate threshold below which estuary Sr/Ca increases rapidly to near the marine Sr/Ca at low salinities (5‰–15‰), thereby providing sharp delineation of estuary entrance, but little to no discrimination among higher salinity habitats. Our approach provides a general framework for assessing the potential utility of Sr/Ca in estuarine systems and specifically for the San Francisco Estuary.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".