Biogeochemical tags in fish: predicting spatial variations in strontium and manganese in <i>Salmo trutta</i> scales using stream water geochemistry
Bibliographic record
Abstract
Fish scales of Salmo trutta exhibited regional patterning in Sr and Mn concentrations in major third-order tributaries in a small upland catchment (the Dee catchment in Wales, United Kingdom; drainage area ∼1800 km2) that appeared to reflect regional variability in catchment geology and stream water chemistry. When baseline signatures in scale element concentrations were established at a number of sites (n = 12) among the upper, middle, and lower regions of the Dee catchment, 73% of fish were classified to their region of capture based on Sr and Mn in scales. However, the regional patterning in scale chemistry was degraded when high-resolution catchment-wide variability in scale element concentrations was predicted using element concentrations in stream water as a proxy for Sr and Mn in scales at 792 sites in first-, second-, and third-order tributaries in the catchment (mean distance between neighbouring sites = 738 m, range = 41–3634 m). This analysis indicated that many locations throughout the catchment could be potential source locations and demonstrates that the initial classification accuracy was artificially inflated. We have illustrated limitations of the site-based study design commonly employed in biogeochemical tagging studies. Future studies should take account of potential variation in baseline signatures at fine geographical scales when determining the accuracy of stock discrimination using biogeochemical tags.
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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) | 0.000 | 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 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".