Validation of dried blood spot sampling for determining trophic positions of Arctic char using nitrogen stable isotope analyses of amino acids
Bibliographic record
Abstract
Rationale Dried blood spots (DBSs) are gaining popularity for biomarker analyses in ecological research due to their advantages for use in field‐based research and in remote settings; however, many DBS biomarkers remain unvalidated. We validated the application of compound‐specific stable nitrogen isotope analyses of amino acids (CSIA‐AAs) to field‐prepared DBSs for determining trophic positions of wild‐caught Arctic char ( Salvelinus alpinus ). Methods Whole blood and muscle from Arctic char were collected, and DBSs were created in the field. We measured the stable nitrogen isotope ratios (expressed as δ 15 N values) of the amino acids glutamic acid (Glu) and phenylalanine (Phe) isolated from Arctic char samples using CSIA‐AAs. We then compared amino acid δ 15 N values from DBSs and the other sample types (whole blood and muscle) from the same specimens. We calculated and compared trophic position estimates generated from whole blood, DBSs, and muscle. Results The δ 15 N values of Glu and Phe, as well as trophic position estimates from DBSs, were highly correlated with δ 15 N values and estimates from both whole blood and muscle. The DBS amino acid δ 15 N values and trophic position estimates agreed well with those from whole blood. Although mean differences between amino acid δ 15 N values from DBSs and muscle were noted, the offsets were small and resulted in a 0.2 mean difference between trophic position estimates for DBSs and muscle. Conclusions We demonstrate that the application of CSIA‐AAs to field‐prepared DBSs of Arctic char generates similar trophic position estimates to those from whole blood and muscle. We suggest that DBSs could be developed as a minimally invasive sampling technique to study feeding ecology of wild fish and perhaps other organisms of interest.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".