Experimentally derived trophic enrichment and discrimination factors for Chinook salmon, <scp> <i>Oncorhynchus tshawytscha</i> </scp>
Bibliographic record
Abstract
RATIONALE: Stable isotope analysis (SIA) can provide important insights into food web structure and is a widely used tool in ecological conservation and management. It has recently been augmented by compound-specific stable isotope analysis of amino acids (CSIA-AA), an innovation that can provide greater precision when analyzing trophic level and food web connectivity. The utility of SIA rests on confidence in its constituent parameters such as the trophic enrichment factor (TEF). There is increasing emphasis on the need to experimentally derive species and tissue specific TEFs for studies utilizing SIA. Chinook salmon, Oncorhynchus tshawytscha, is a species with high potential for study using SIA due to the difficulty in observing its ecology during its marine phase and the significance of the conservation consequences of recent population declines. METHODS: Bulk and amino acid-specific TEFs were determined for juvenile and adult Chinook salmon fed specific diets. Three controlled feeding studies were performed: adult salmon were fed a biofeed, juvenile salmon were fed a biofeed, and juvenile salmon were fed krill. Bulk and compound-specific stable isotope data were collected from diet samples and from salmon muscle tissue after a minimum of 8 weeks of controlled feeding. Bulk isotope signatures were measured using EA-IRMS and CSIA-AA signatures using GC/C-IRMS, allowing the TEFs to be calculated. RESULTS: was 6.67‰. For carbon isotopes of amino acids, the derived TEFs of Iso, Leu and Phe were near 0‰, as was that of Met, supporting their use of as source amino acids in future CSIA studies. CONCLUSIONS: This study presents Chinook salmon-specific TEFs for bulk and amino acid SIA. It supports the application of future research applying SIA to the study of Chinook salmon and validates previous research on species-specific TEFs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".