Can otolith elemental signatures record the capture site of Patagonian toothfish (<i>Dissostichus eleginoides)</i>, a fully marine fish in the Southern Ocean?
Bibliographic record
Abstract
Otolith chemistry has been successfully used to reconstruct the environmental history experienced by estuarine-dependent teleost fish, including movement between estuaries and coastal areas. However, application has been more limited in species exposed exclusively to oceanic waters, where gradients in physical and chemical properties are less extreme. To test whether otolith elemental signatures record spatial information in an oceanic species, we sampled otoliths from Patagonian toothfish (Dissostichus eleginoides) and used an inductively coupled plasma mass spectrometer (ICP-MS) coupled to a laser ablation system to target the outer otolith edges corresponding to the period immediately before capture. Using multivariate analysis of variance and multivariate discriminant analysis, we found that edge signatures discriminated toothfish by geographic region with near complete success: only 5% of fish caught off South America and in the Antarctic were misclassified to sampling areas in the other region. Moreover, edge signatures showed strong differences between sampling areas within each region: fish captured off South America classified to sampling areas therein with 79%84% success, and Antarctic fish classified to sampling areas therein with 50%67% success. These results compare favourably with rates of classification for estuarine-dependent fish, demonstrating that otolith elemental signatures can discriminate the geographic provenance of oceanic and estuarine-dependent fish.
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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 | 0.000 |
| 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".