Contribution of lake littoral zones to the continental carbon budget
Bibliographic record
Abstract
Abstract In the littoral zone, at the land–water interface of lakes, the areal productivity of aquatic vegetation rivals that of rainforests, resulting in a potentially very high carbon (C) turnover. Whereas tidal wetlands at the land–ocean interface are included in global C budgets, lake littoral zones are currently not accounted for, despite the total shoreline of lakes being estimated at four times longer than that of the global ocean. Here we quantify the littoral net atmospheric C sink using mass balance and a model of C export from the littoral to the pelagic zone. We argue that ignoring littoral C turnover in lakes potentially results in biased estimates of continental C cycling. In our global estimate, we show that the estimated global C balance of lakes may reverse from a net C source to a net C sink (that is, net C burial > net C outgassing). In addition, a large part of the C outgassed in the pelagic might originate from the littoral, implying that previous estimates of terrestrial C inputs to inland waters were too high. We argue that quantifying and modelling lake littoral C fluxes are essential to more accurately estimate the feedback between the continents and climate.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".