Nitrogen uptake by plants subsidized by Pacific salmon carcasses: a hierarchical experiment
Bibliographic record
Abstract
Bears ( Ursus spp.) and other predators can capture and transport large numbers of Pacific salmon ( Oncorhynchus spp.) to riparian areas beside small coastal streams, a process that may affect site productivity and local plant communities. We used a novel experimental manipulation of salmon carcasses to analyze understory plant uptake of salmon-derived N. A hierarchical before–after, control–impact design was used with the addition of chum salmon ( Oncorhynchus keta (Walbaum in Artedi, 1792)) carcasses to forest sites along 11 streams on the central coast of British Columbia, Canada. Eight months after carcass placement, the foliar %N and δ15N in three herbaceous and one moss species had increased by an average of 14%–60% (%N) and 0.5–3.3‰ (δ15N) at treatment carcass sites versus control sites. Treatment effects for %N were typically greater than for δ15N. Nitrogen isotope signatures at carcass sites were highly variable (δ15N range = 30.1‰) and were mediated by plant species, stream salmon density, carcass mass, and individual plant foliar %N. These results show that understory plants use N from salmon during an important period of plant growth many months after carcasses were deposited in riparian areas. However, they also indicate that habitat variation across spatial scales from individual plants to streams needs to be considered when estimating the contribution of salmon to plant nutrition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".