Elevated levels of δ<sup>15</sup>N in riverine Painted Turtles (<i>Chrysemys</i> <i>picta</i>): trophic enrichment or anthropogenic input?
Bibliographic record
Abstract
The natural abundance of stable isotopes of elements in animal tissue is influenced by both biotic and abiotic factors. Biotically, animals feeding at higher trophic levels are enriched in the ratio of 15N:14N (δ15N) relative to their food resources owing to the preferential excretion of 14N. Abiotically, increases in δ15N may also reflect different sources of biologically available nitrogen, including nitrogen resulting from denitrification of inorganic fertilizer. We studied variation in δ15N among freshwater turtle populations to assess spatial variation in δ15N and to determine whether this variation can be attributed to differences in nitrogen source or trophic enrichment. We examined nitrogen and carbon stable isotope ratios in duckweed (genus Lemna L.) and in Painted Turtles (Chrysemys picta (Schneider, 1783)) in aquatic ecosystems expected to be differentially affected by agricultural activity and denitrification of inorganic fertilizer. Across sites, C. picta δ15N was strongly correlated with Lemna δ15N and was elevated in sites influenced by agricultural activity. Furthermore, trophic position of turtles was not associated with δ15N but was consistent with expected values for primary consumers in freshwater systems, indicating that differences in tissue δ15N could be attributed to differences in initial sources of nitrogen in each ecosystem. Our results suggest that care must be taken when attributing differences in isotopic values of animal populations to trophic factors.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".