Trash or treasure? Use of sagittal otoliths partially composed of vaterite for hatchery stock discrimination in steelhead
Bibliographic record
Abstract
Sagittal otoliths are normally deposited as the CaCO3 polymorph aragonite; however, a proportion of otoliths transitions to vaterite during growth. This transition can complicate otolith chemistry analyses, as differences in the crystalline structure (aragonite or vaterite) of otoliths causes variation in otolith chemistry signatures. To address this issue, we introduce a method to utilize sagittal otoliths partially composed of vaterite for stock discrimination. Using this method, we determined the hatchery origins of yearlings from five Lake Erie hatcheries by using Ba, Mg, Mn, and Sr concentrations in vaterite sections of steelhead (Oncorhynchus mykiss) otoliths. We then compared the classification accuracy of our vaterite method with a method in which otoliths composed entirely of aragonite were used. Overall, quadratic discriminant function analyses revealed similar classification success when elemental concentrations from vaterite (95% accuracy) and aragonite (94% accuracy) otolith regions were used. The methods introduced here could likely be used for other fish species that have otoliths that transition to vaterite as long as an adequate number of juvenile fish are available to develop vaterite otolith chemistry signatures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".