Mixing and its effects on biogeochemistry in the persistently stratified, deep, tropical Lake Matano, Indonesia
Bibliographic record
Abstract
In the > 590‐m deep, tropical Lake Matano (Indonesia), stratification is characterized by weak thermal gradients (< 2°C per 500 m) and weak salinity gradients (< 0.14% per 500 m). These gradients persist over seasons, decades, and possibly centuries. Under these nearly steady‐state conditions, vertical eddy diffusion coefficients (Kz) cannot be estimated by conventional methods that rely on time derivatives of temperature distributions. We use and compare several alternative methods: one‐dimensional k‐ε modeling, three‐dimensional hydrodynamic modeling, correlation with the size of Thorpe instabilities, and correlation with the stability frequency. In the thermocline region, at 100‐m depth, the Kz is ~ 5 × 10−6 m2 s−1, but, below 300 m, the small density gradient results in large (20 m) vertical eddies and high mixing rates (Kz ~ 10−2 m2 s−1). The estimated timescale of water renewal in the monimolimnion is several hundred years. Intense evaporation depletes the surface mixed layer of 16O and 1H isotopes, making it isotopically heavier. The lake waters become progressively isotopically lighter with depth, and the isotopic composition in the deep waters is close to those of the ground and tributary waters. The vertical distribution of Kz is used in a biogeochemical reaction‐transport model. We show that, outside of a narrow thermocline region, the vertical distributions of dissolved oxygen, iron, methane, and phosphorus are shaped by vertical variations in transport rates, rather than by sources or sinks.
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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 | 0.001 |
| 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".