Stratification and horizontal exchange in Lake Victoria, East Africa
Bibliographic record
Abstract
We characterize stratification patterns over diel, seasonal, and annual time scales in inshore and offshore regions of Lake Victoria, East Africa; determine conditions leading to horizontal exchanges; and, using surface energy budgets derived from local meteorological stations and two reanalysis products, address whether stratification depends on advective as opposed to local processes. The largest change in the surface energy budget occurred when winds intensified at the end of the long rains, with the wind's intensification, duration, and spatial extent dependent on El Niño–Southern Oscillation cycles. These winds flush inshore waters and cause cross‐basin upwelling similar to that observed in the deep African Great Lakes. Wedderburn numbers indicated mixing and cross‐basin within‐thermocline transport. The internal wave‐induced mixing and enhanced latent heat fluxes of −300 to −400 W m −2 contributed to the loss of seasonal stratification. Advection of cool water was required to balance the heat budget of northern offshore waters in the latter half of the southeast monsoon except in an El Niño year. Northern waters became weakly stratified after the southeast monsoon, with nocturnal winds contributing to heat transport and ventilation of the lower water column. Following the rainy season, downwelling by sustained southerly albeit low winds is a likely cause of the seasonal thermocline. Inshore waters are 0.2–1.5°C warmer than those offshore, conditions conducive to horizontal convective circulation except during onshore winds. The seasonal cycle of stratification and inshore–offshore and cross‐basin exchanges are moderated by differential heating, cooling, and basin‐scale thermocline tilting.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".