Cascade reservoirs on nitrogen fractions in upper Mekong sediments against natural river
Bibliographic record
Abstract
The construction of cascade hydropower dams in the Lancang River Basin of Southwest China significantly affects sediment nitrogen fractions. To assess these impacts, we contrasted the dammed Lancang River with the undammed Nujiang River. Using the recommended Ruttenberg sequential extraction process, we determined five nitrogen fractions: free nitrogen (F-N), exchangeable nitrogen (Ex-N), carbonate-associated nitrogen (CO3-N), ironmanganese oxides-bound nitrogen (IM-N), and organic nitrogen (Org-N). There were considerable differences between the properties of sediments across the two rivers and had their effects on nitrogen fraction distribution. The cascade reservoirs possessed higher levels of transferable nitrogen (TranN) and total inorganic nitrogen (TIN) than the natural river section. Nitrogen composition in the natural river was as per Org-N > CO3-N > IM-N > Ex-N > F-N, but at certain points in reservoirs, it altered to Org-N > IM-N > CO3-N > Ex-N > F-N. This means that cascade reservoirs favor the release of nitrogen and enhance bioavailable nitrogen. Dynamics of nitrogen fractions depend on environmental factors such as altered water level, reduced flow velocity, silt interception, resuspension of sediment, particle-size distribution, and mineral composition, and axis I explains over 70% of variance.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".