Evaluation of isotopic fractionation error on calculations of marine-derived nitrogen in terrestrial ecosystems
Bibliographic record
Abstract
Pacific salmon (Oncorhynchus spp.) transport nitrogen (N) from oceans to inland ecosystems. Salmon δ15N is higher than δ15N expected in terrestrial plants, so linear two-source mixing models have commonly been used to quantify contributions of marine-derived N (MDN) to riparian ecosystems based on riparian plant δ15N. However, isotopic fractionation potentially contributes to error in MDN estimates by changing δ15N of salmon-derived N appearing in soil and plants. We used a simulation model to examine potential effects of fractionation on MDN estimates. We also measured changes in δ15N and δ13C as N and carbon (C) moved from bear feces into soil, and compared MDN estimates using three different estimates for the marine endmember of a linear mixing model. Simulation demonstrated that fractionation during soil N losses could lead to large overestimations of MDN when δ15N of salmon tissue is used as the marine endmember. δ15N of bear feces was significantly enriched (by 1.9) relative to salmon tissue, but did not change during movement of feces-derived N into soil. In contrast, δ13C decreased by 1.9 between salmon and bear feces and declined an additional 4.2 during movement into soil. We propose a new method for estimating the δ15N of the marine endmember that accounts for isotope fractionation occurring as marine N is cycled in soil. This method uses the proportional difference in soil 15N content between reference and spawning sites to calculate the marine endmember δ15N.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".