δ<sup>15</sup>N in riverine food webs: effects of N inputs from agricultural watersheds
Bibliographic record
Abstract
We examined the use of the natural abundance of nitrogen stable isotopes (δ15N) as a tracer of anthropo genic perturbations of the nitrogen (N) cycle at the watershed scale in 82 river sites draining 13 watersheds in the St. Lawrence Lowlands in Quebec. Mean δ15N values of aquatic primary consumers varied greatly among sites (+2 to +15), most of this variation (88%) being attributable to site effects. Variation in δ15N values among functional feeding groups of primary consumers within sites was comparatively lower (<1). Within watersheds, δ15N values of primary consumers (and organisms of higher trophic levels) tracked longitudinal changes in the percentage of agricultural area. Overall, the percentage of total watershed area under agriculture explained up to 69% of the variation in mean primary consumer δ15N values. Similar positive correlations were observed for predatory invertebrates and non-piscivorous fish. In general, our results show that δ15N in riverine food webs reacts strongly to spatial patterns in the intensity of N inputs related to agricultural land use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".