Stratification stability of tropical lakes and their sensitivity to climate
Bibliographic record
Abstract
Abstract Tropical lakes experience less thermal convection than temperate lakes and can be stably stratified by temperature gradients alone. Deep tropical lakes are often presumed meromictic, but conditions for sustained meromixis are poorly constrained. Here, we investigate the stratification stability of a deep (200 m) tropical Lake Towuti (Indonesia) and analyze its sensitivity to meteorological and climatic parameters using exploratory hydrodynamic simulations. The inferred effects on water chemistry are further evaluated with a reactive‐transport biogeochemical model. In contrast to the previous assumption of meromixis, our results suggest that in 2008 Lake Towuti experienced mixing over most of its water column, with the attendant weak oxygenation of the previously anoxic lower strata. The lake likely remained stratified afterwards, but the chemical distributions measured after the episode, particularly during the International Continental Scientific Drilling Program in 2015, were out of steady state, and may be still evolving in time. We find the thermal stratification to be most sensitive to average air temperatures and wind speeds and predict further stabilization of the lake by climate change. We also describe a negative feedback mechanism that can stabilize oligomictic tropical lakes for a number of years after the episodes of mixing and cooling, and infer that such episodes should be significantly less frequent in deeper lakes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".