Comparing naturally occurring stable isotopes of nitrogen, carbon, and strontium as markers for the rearing locations of Atlantic salmon (<i>Salmo salar</i>)
Bibliographic record
Abstract
We compared the success of using naturally occurring stable isotopes of N, C, and Sr as markers for the rearing locations of juvenile salmon. We analyzed the isotopic signatures (δ15N and δ13C in muscle and scales and 87Sr/86Sr in otoliths) of >200 juvenile Atlantic salmon (Salmo salar) from 12 tributaries of the Connecticut River, USA. Young salmon had distinct N and C signatures 5 weeks after stocking. Signatures were stable over the summer although δ13C varied more than δ15N or 87Sr/86Sr. Scale and muscle signatures were highly correlated, demonstrating the feasibility of nonlethal sampling using fish scales. Some C (but not N) signature from the hatchery was retained in scales of 3-month-old fish, implicating scale annuli as a repository for past C signatures. The δ15N values successfully differentiated fish from tributaries with differences in land use (e.g., agricultural versus forested; ≈ 33% of sites); the δ13C values differentiated fish from 45% of sites. Based upon a discriminant-function analysis, group membership of individuals was correctly predicted in 44.3% (74 of 167) of cases for which both N and C were analyzed. In combination, N and C isotopes differentiated 73% of study sites, which was close to the success of Sr isotopes in the same system (83%).
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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 | 0.002 |
| 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".