Worldwide retention of nutrient silicon by river damming: From sparse data set to global estimate
Bibliographic record
Abstract
Abstract Damming of rivers represents a major anthropogenic perturbation of the hydrological cycle, with the potential to profoundly modify the availability of nutrient silicon (Si) in streams, lakes, and coastal areas. A global assessment of the impact of dams on river Si fluxes, however, is limited by the sparse data set on Si budgets for reservoirs. To alleviate this limitation, we use existing data on dissolved Si (DSi) retention by dams to calibrate a mechanistic model for the biogeochemical cycling of DSi and reactive particulate Si (PSi) in reservoir systems. The model calibration yields a relationship between the annual in‐reservoir siliceous primary productivity and the external DSi supply. With this relationship and an estimate of catchment Si loading, the model calculates the total reactive Si (RSi = DSi + PSi) retention for any given reservoir. A Monte Carlo analysis accounts for the effects of variations in reservoir characteristics and generates a global relationship that predicts the average reactive Si retention in reservoirs as a function of the water residence time. This relationship is applied to the Global Reservoirs and Dams database to estimate Si retention by damming worldwide. According to the results, dams retain 163 Gmol yr −1 (9.8 Tg SiO 2 yr −1 ) of DSi and 372 Gmol yr −1 (22.3 Tg SiO 2 yr −1 ) of RSi, or 5.3% of the global RSi loading to rivers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".