Sediment organic carbon burial in agriculturally eutrophic impoundments over the last century
Bibliographic record
Abstract
We estimated organic carbon (OC) burial over the past century in 40 impoundments in one of the most intensively agricultural regions of the world. The volume of sediment deposited per unit time varied as a function of lake and watershed size, but smaller impoundments had greater deposition and accumulation rates per unit area. Annual water storage losses varied from 0.1–20% and were negatively correlated with impoundment size. Estimated sediment OC content was greatest in lakes with low ratios of watershed to impoundment area. Sediment OC burial rates were higher than those assumed for fertile impoundments by previous studies and were much higher than those measured in natural lakes. OC burial ranged from a high of 17,000 g C m −2 a −1 to a low of 148 g C m −2 a −1 and was significantly greater in small impoundments than large ones. The OC buried in these lakes originates in both autochthonous and allochthonous production. These analyses suggest that OC sequestration in moderate to large impoundments may be double the rate assumed in previous analyses. Extrapolation suggests that they may bury 4 times as much carbon (C) as the world's oceans. The world's farm ponds alone may bury more OC than the oceans and 33% as much as the world's rivers deliver to the sea.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".