Temporal and spatial variability in elemental composition of otoliths: implications for determining stock identity and connectivity of populations
Bibliographic record
Abstract
Interannual variability in elemental composition of otoliths may confound spatial interpretations. The elemental fingerprints of otoliths of juvenile fish were determined for fish collected from 12 to 15 estuaries in each of three consecutive recruitment years to determine temporal variation in otolith chemistry for each estuary. It was also examined whether there is overlap in elemental fingerprints of fish collected in different years and from different estuaries that may confound subsequent spatial comparisons. Significant differences in otolith chemistry were found among years for individual elements (lithium, manganese, strontium, and barium) and for multi-element fingerprints. Some estuaries showed large variation in multi-element fingerprints among years, whereas others showed little variation among years. There was some overlap of elemental fingerprints of different estuaries, but these were not always for fish collected in the same year. The significant spatial and temporal variation in elemental fingerprints meant that it was possible to confound spatial differences with temporal differences. Therefore, if the natal estuary of the adults is to be determined, a library of elemental fingerprints needs to be built up over time for each estuary rather than a single year-class of juveniles being used as the elemental fingerprint for a number of year-classes of adults.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".