Spatial variability of nutrient concentrations, fluxes, and ecosystem metabolism in Nyanza Gulf and Rusinga Channel, Lake Victoria (East Africa)
Bibliographic record
Abstract
Measurements of nutrient concentrations and physicochemical parameters were carried out monthly between March 2005 and March 2006 along a transect between the Nyanza Gulf and the open waters of Lake Victoria to evaluate spatial and seasonal variations in nutrient fluxes and ecosystem metabolism. Nutrient concentration gradients were observed between the deep and seasonally stratifying main lake basin and the large, shallow river‐influenced Nyanza Gulf, which are connected by the relatively deep and narrow Rusinga Channel. Nutrient gradients were steepest in the Rusinga Channel where there was always a strong gradient in electrical conductivity. A mixing‐box model used to quantify nutrient fluxes and ecosystem metabolism along the gulf and the channel showed that nutrients entering the gulf through river inflows and municipal sources were largely retained in the gulf, with only a small fraction transferred into the main lake. In contrast, the main lake was a net exporter of dissolved inorganic phosphorus to the gulf (110 mg P m −2 yr −1 ), which is in contrast to the paradigm that the gulf is a major contributor to the increasing nutrient enrichment and, hence, eutrophication of the greater Lake Victoria. The channel had net regeneration of dissolved nutrients indicating net heterotrophy; whereas in the gulf, there was net consumption of dissolved nutrients, resulting in high estimates of net ecosystem production (570 mg C m −2 d −1 ) and, hence, net autotrophy. Nitrogen balance ranged from +3 to +15 mg N m −2 d −1 , indicating dominance of nitrogen fixation over denitrification in both the gulf and the channel, which are both well‐oxygenated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".