A desiccating saline lake bed is a significant source of anthropogenic greenhouse gas emissions
Bibliographic record
Abstract
Saline lake desiccation is widespread and typically caused by anthropogenic withdrawals for agricultural, industrial, and municipal uses, but its impact on greenhouse gas (GHG) emissions is unknown. While dry-flux studies have shown that desiccating waterbodies emit carbon dioxide (CO 2 ) and methane (CH 4 ) from exposed sediments, these studies are often seasonal and for freshwater systems, limiting their application to chronically desiccating saline lakes. We measured CO 2 and CH 4 emissions (April to November, 2020) from the exposed sediments of Great Salt Lake (Utah, United States), and compared them with aquatic emissions estimates to determine the anthropogenic emissions associated with desiccation. In 2020, the lake bed emitted 4.1 million tons of CO 2eq to the atmosphere, primarily (94%) as CO 2 , constituting a ∼7% increase to Utah's anthropogenic GHG emissions. As climate change exacerbates drought in arid regions, anthropogenic desiccation and associated climate feedbacks should be considered in assessments of global GHG trajectories as well as local GHG emissions reduction efforts.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".