Seasonally resolved environmental reconstructions using fish otoliths
Bibliographic record
Abstract
Exploiting the chemical and growth properties of otoliths, this study demonstrates how environmental archives with high temporal resolution can be developed. Elemental profiles (Ba:Ca and Sr:Ca) of fish otoliths (ear bones) from the estuarine species Acanthopagrus butcheri (black bream) were related to growth increments on a seasonal time scale. A series of mixed effects models were used to investigate biological, temporal, and environmental factors influencing seasonal otolith elemental profiles. Resultant seasonally resolved chemical chronologies were correlated with environmental data (i.e., salinity) to develop an element–salinity regression function, which when fit to an independently derived chemical chronology showed strong agreement between reconstructed and recorded salinities. Support for the element–salinity regression function through independent verification provided confidence in environmental reconstructions derived from an archaeological otolith. This suggests otoliths can be used to reconstruct past environmental conditions over decadal and centennial time scales. Moreover, the application of mixed effect models to develop chemical chronologies also provides information on drivers of elemental profiles and allows a range of ecological questions to be addressed. This approach may be further adapted and employed across a broader range of taxonomic groups and environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".