The effects of salmon carcasses on soil nitrogen pools in a riparian forest of southeastern Alaska
Bibliographic record
Abstract
Long-term studies in Alaska have demonstrated that bears may capture and carry to the riparian forest a large number of salmon that return to spawn in small freshwater streams. Most of the carcasses are partially consumed, resulting in a large amount of salmon nutrients in the form of biomass deposited on the forest floor. Using an experimental approach, we examined how these carcasses may influence the spatial and temporal dynamics of soil C and N in a riparian forest in southeastern Alaska. At their peak, ammonium (NH4+-N) concentrations in soil 10 cm from carcasses were as much as several orders of magnitude greater than soils in adjacent control plots without carcasses and remained elevated until the onset of winter. Nitrate (NO3–-N) and δ15N concentrations also increased coincident with maximum NH4+-N concentrations. However, soil N concentrations were only moderately elevated 20 cm from carcasses and closely resembled background concentrations at 30 cm. These results suggest that salmon carcasses, via bear foraging activities, can dramatically influence soil N pools, although the impacts appear to be highly localized and largely dependent on the spatial distribution of carcasses in the riparian forest.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".