Structure and composition of yellowtail kingfish (<i>Seriola lalandi</i>) otoliths: implications for using aquaculture‐reared fish to reconstruct life histories
Bibliographic record
Abstract
Understanding fish life history is essential for effective management of fisheries, but continuous tracking over lifetime temporal scales can be difficult. Fish otoliths contain a natural biogeochemical record of ambient environmental conditions and habitat use over such scales. However, ecological interpretations of these elemental compositions can be influenced by the structural composition of calcium carbonate otoliths, which can vary between wild and aquaculture fish as well as across species. As such, we used in situ confocal micro-Raman spectroscopy and powder X-ray diffraction to assess and quantify, respectively, the calcium carbonate polymorphs of the otoliths of aquaculture and wild yellowtail kingfish (Seriola lalandi) to validate whether the otoliths from aquaculture fish can be used to interpret patterns seen in the otoliths of wild fish. We then analysed major and minor trace elements using wavelength dispersive X-ray spectroscopy to investigate the potential habitat use of wild S. lalandi. The elemental composition of S. lalandi otoliths was examined across three cross-sectioned growth zones: the first (i.e. oldest) annulus (growth ring), the outermost (i.e. most recent) annulus and mid-way between these. All otoliths were shown to be composed of aragonite, with the exception of small proportions (0.5 wt%) of vaterite in a single wild S. lalandi otolith. This finding indicates that otoliths of aquaculture S. lalandi, reared in known conditions, are appropriate to interpret stable isotope analyses in wild S. lalandi. Wild-caught S. lalandi otoliths had concentrations of barium and manganese below detectable limits (<0.2 and <0.01 wt%, respectively) inferring that S. lalandi were not in estuarine or brackish environments when the analysed annuli were formed, suggesting these environments are not used for spawning.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".