A critical review of the effect of water storage reservoirs on organic matter decomposition in rivers
Bibliographic record
Abstract
Organic matter decomposition is vital in sustaining river food webs. However, little is known about the effect of water storage reservoirs on organic matter decomposition in rivers. In this paper, we reviewed and analyzed 37 studies that investigated the effect of man-made reservoirs on organic matter decomposition in rivers. Most studies focused on decomposition of tree leaf litter (54.1%) and macrophytes litter (43.2%), while fewer studies evaluated decomposition of wood (2.7%). Based on qualitative analysis, the effect of small weirs on organic matter decomposition is local and the effect on most habitat variables is minimal. Mean effect sizes (Hedges’ g) for organic matter decomposition were −1.98 for small weirs, −1.31 for small reservoirs, and −0.66 for large reservoirs. This review demonstrates that, in general, reservoirs have a negative effect on litter decomposition. Litter decomposition, an important ecosystem process, is sensitive to impacts of reservoirs in different types of rivers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.013 |
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; both teacher heads agree on what is shown here.
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".