The role of sediments in the carbon budget of a small boreal lake
Bibliographic record
Abstract
We investigated the role of lake sediments as carbon (C) source and sink in the annual C budget of a small (0.07 km2) and shallow (mean depth, 3.4 m), humic lake in boreal Sweden. Organic carbon (OC) burial and mineralization in the sediments were quantified from 210Pb-dated sediment and laboratory sediment incubation experiments, respectively. Burial and mineralization rates were then upscaled to the entire basin and to one whole year using sediment thickness derived from sub-bottom profiling, basin morphometry, and water column monitoring data of temperature and oxygen concentration. Furthermore, catchment C import, open water metabolism, photochemical mineralization as well as carbon dioxide (CO2) and methane (CH4) emissions to the atmosphere were quantified to relate sediment processes to other lake C fluxes. We found that on a whole-basin and annual scale, sediment OC mineralization was three times larger than OC burial, and contributed about 16% to the annual CO2 emission. Other contributions to CO2 emission were water column metabolism (31%), photochemical mineralization (6%), and catchment imports via inlet streams and inflow of shallow groundwater (22%). The remainder (25%) could not be explained by our flux calculations, but was most likely attributed to an underestimation in groundwater inflow. We conclude that on an annual and whole-basin scale (1) sediment OC mineralization dominated over OC burial, (2) water column OC mineralization contributed more to lake CO2 emission than sediment OC mineralization, and (3) catchment import of C to the lake was greater than lake-internal C cycling.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".