Direct versus indirect pathways of salmon-derived nutrient incorporation in experimental lotic food webs
Bibliographic record
Abstract
The objective of our study was to examine how salmon carcass subsidization through alternative trophic pathways affected stream food web productivity. Three salmon carcass treatments (dissolved carcass nutrients only, dissolved carcass nutrients + carcass material, and a carcass-free control) were replicated five times in artificial stream channels. We quantified changes in water chemistry and the production of periphyton, macroinvertebrates, and age 0+ steelhead trout (i.e., sea-run rainbow trout, Oncorhynchus mykiss ) over 65 days. Dissolved ammonium and periphyton chlorophyll a concentrations increased for an approximate 2-week period in both carcass-augmented treatments. However, there was no commensurate effect on periphyton ash-free dry mass. Total macroinvertebrate biomass was significantly greater in the presence of nutrients + material after 65 days, but no such increase was observed in response to the addition of dissolved nutrients only. Despite modest and inconsistent effects at lower trophic levels, both the nutrients only and nutrients + material treatments increased the growth and condition of age 0+ steelhead trout, with significantly greater gains occurring in the presence of nutrients + material. These data suggest that while salmon carcasses can enhance the short-term growth of juvenile salmonids via bottom-up pathways, the availability and direct consumption of carcass biomass may promote a substantial amount of additional production.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".