Biofilm nutrient limitation, metabolism, and standing crop responses to experimental application of salmon carcass analog in Idaho streams
Bibliographic record
Abstract
Dramatic declines of Pacific salmon (Oncorhynchus spp.) populations have decreased delivery of marine-derived material to Pacific Northwest streams where juvenile salmon reside. Managers use artificial nutrient additions to increase juvenile salmon growth and survival and typically assume nutrient-driven increases in biofilm production are an important pathway by which nutrients become available to higher trophic levels. To evaluate how biofilms respond to additions of salmon carcass analog, a pasteurized, processed form of nutrient mitigation materials, we quantified biofilm nutrient limitation, benthic and whole-stream metabolism, and biofilm standing crops before and following experimental additions in tributaries of the Salmon River, Idaho, USA. Biofilm nutrient limitation did not change and standing crop did not increase in response to analog additions at two different levels (low, 30 g·m−2; or high, 150 g·m−2) within 1 month of addition. In contrast, whole-stream and benthic primary productivity and respiration increased in a high-analog treated segment, but did not increase in a low-analog treated segment. Together, our results suggest that metabolism may be a more appropriate tool for assessing the ecosystem effects of nutrient additions than biofilm standing crop or nutrient limitation, which are constrained by a variety of abiotic and biotic factors like hydrology and grazing.
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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 | 0.001 |
| 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".