Capitalizing on the wealth of chemical data in the accretionary structures of aquatic taxa: Opportunities from across the tree of life
Bibliographic record
Abstract
Abstract Aquatic organisms are natural data loggers and record chemical variations within hardened accretionary structures like shells and teeth. Chemical sclerochronology is the study of these chemical variations through time and how they are used to understand environmental change and the physiology and ecology of species. While sclerochronology research has largely focused on bivalves, teleost fish, and hard corals, there are many other aquatic taxa rich with time‐resolved chemical data. To expand focus to these “other” taxa and determine the state‐of‐play, we compiled a database of chemical sclerochronology studies spanning nine living phyla and 19 classes. We then examined research trends and knowledge gaps across these taxa and showcase their exciting potential to collect critical data and address pressing environmental and ecological challenges. We hope this synthesis will encourage further research on species across the tree of life, as well as foster collaboration among the established and lesser‐known fields of sclerochronology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".