River management alters ecosystem metabolism in a large oligotrophic river
Bibliographic record
Abstract
Algae and aquatic plants support river food webs through in-situ primary production. However, gross primary production (GPP) and ecosystem respiration (ER) are rarely evaluated in the context of river management or habitat restoration. We estimated daily GPP and ER during 2 growing seasons for 7 reaches in the Kootenai River and 1 reach in the Elk River, spanning 290 river km across British Columbia, Canada, and Montana and Idaho, USA. We characterized responses of GPP and ER to river management, including reaches with unregulated flow, regulated flow, nutrient addition, and habitat restoration. Downstream GPP and ER generally increased after changes in river management, and higher management intensity led to greater increases. GPP and ER followed a seasonal pattern with low initial values in spring, elevated values in mid-summer, and a return to low values in late summer and autumn. Timing and duration of the elevated period for GPP and ER also differed among reaches following changes in river management. Our results suggest that river management affects GPP and ER, likely through reducing turbidity and the frequency and magnitude of extreme flow events, nutrient additions, and enhanced floodplain connectivity, thereby altering the timing and amount of autochthonous carbon available to the food web.
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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".