Comparison of the performance of scale and otolith microchemistry as fisheries research tools in a small upland catchment
Bibliographic record
Abstract
Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) analysis of recently formed Salmo trutta scale hydroxyapatite and otolith aragonite provided biogeochemical tags of S. trutta at six sites (>7.5 km apart) in a small upland catchment (drainage area: ~1800 km2). 87% and 89% of fish were correctly classified to their site of capture based on Sr, Mn, Ba, and Mg concentrations in scales and otoliths, respectively. Sr, Mn, and Ba were highly significantly correlated between structures of the same fish (P < 0.001). Ba and Mn in both structures were significantly correlated with stream water chemistries at each site (P < 0.05). Significant differences among sites were found in 11 element concentrations in scales and six element concentrations in otoliths (P < 0.05). Broadening the suite of elements improved the classification to 90% when using otoliths and 92% when using scales. Although there appears to be some degree of postdepositional change in scale hydroxyapatite in sea-run S. trutta, it was not sufficient to completely overprint the freshwater signature. Scales offer a nonlethal sampling alternative to otoliths and appear to provide a biogeochemical tag comparable in performance, but further work needs to examine the degree of postdepositional change in scale hydroxyapatite.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".