Does timber harvest influence the dynamics of marine-derived nutrients in Southeast Alaska streams?
Bibliographic record
Abstract
Streams often rely on nutrient subsidies, and variation in nutrient delivery may alter the ecosystem response. Pacific salmon (Oncorhynchus spp.) provide marine-derived nutrients to their natal streams but also cause benthic disturbance, with the net effect determined by watershed and stream characteristics. To understand the factors contributing to variation in salmon-derived nutrients (SDN), we studied nutrient concentration and export in seven streams with varying physical characteristics due to timber harvest (e.g., channel complexity) over three years in Southeast Alaska, USA. Salmon increased concentrations and export of dissolved and particulate nitrogen, phosphorus, and carbon, but the magnitude of increase varied up to 41-fold among streams. The density of live salmon best predicted the increase in nutrient concentration and export, whereas the density of carcasses had a negligible effect. Nutrient export was predicted by transient storage before and after the salmon run. Streams in harvested watersheds with simplified channels had greater nutrient export than those in pristine watersheds with complex channels. However, enrichment from salmon overrode the effect of timber harvest on export during the run. Our study demonstrates that enrichment via SDN is short-lived and related to run size, whereas timber harvest and carcasses exert little influence on SDN dynamics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".