Organic sedimentation in modern lacustrine systems: A case study from Lake Malawi, East Africa
Bibliographic record
Abstract
This study examines the relationship between depositional environment and sedimentary organic geochemistry in Lake Malawi, East Africa, and evaluates the relative significance of the various processes that control sedimentary organic matter (OM) in lacustrine systems. Total organic carbon (TOC) concentrations in recent sediments from Lake Malawi range from 0.01 to 8.80 wt% and average 2.83 wt% for surface sediments and 2.35 wt% for shallow core sediments. Hydrogen index (HI) values as determined by Rock-Eval pyrolysis range from 0 to 756 mg HC g−1 TOC and average 205 mg HC g−1 TOC for surface sediments and 228 mg HC g−1 TOC for shallow core samples. On average, variations in primary productivity throughout the lake may account for ~33% of the TOC content in Lake Malawi sediments (as much as 1 wt% TOC), and have little or no impact on sedimentary HI values. Similarly, ~33% to 66% of the variation in TOC content in Lake Malawi sediments appears to be controlled by anoxic preservation of OM (~1–2 wt% TOC), although some component of the water depth–TOC relationship may be due to physical sediment transport processes. Furthermore, anoxic preservation has a minimal effect on HI values in Lake Malawi sediments. Dilution of OM by inorganic sediment may account for ~16% of variability in TOC content in Lake Malawi sediments (~0.5 wt% TOC). The effect of inputs of terrestrial sediment on the organic character of surface sediments in these lakes is highly variable, and appears to be more closely related to the local depositional environment than the regional flux of terrestrial OM. Total nitrogen and TOC content in surface sediments collected throughout the lake are found to be highly correlated (r2 = 0.95), indicating a well-homogenized source of OM to the lake bottom. The recurring suspension and deposition of terrestrial sediment may account for significant amounts of OM deposited in offshore regions of the lake. This process effectively separates denser inorganic sediment from less dense OM and allows terrestrial OM to preferentially be transported farther offshore. The conclusion is that for the organic carbon content in these regions to be elevated a mixed terrestrial-lacustrine origin is required. The hydrodynamic separation of mineral and organic constituents is most pronounced in regions with shallow bathymetric gradients, consistent with previous findings from Lake Tanganyika.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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 teacher head, 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".