No long‐term trends in <i>p</i>CO<sub>2</sub> despite increasing organic carbon concentrations in boreal lakes, streams, and rivers
Bibliographic record
Abstract
Abstract Concentrations of dissolved organic carbon (DOC) from terrestrial sources have been increasing in freshwaters across large parts of the boreal region. According to results from large‐scale field and detailed laboratory studies, such a DOC increase could potentially stimulate carbon dioxide (CO2) production, subsequently increasing the partial pressure of CO2 (pCO2) in freshwaters. However, the response of pCO2 to the presently observed long‐term increase in DOC in freshwaters is still unknown. Here we tested whether the commonly found spatial DOC‐pCO2 relationship is also valid on a temporal scale. Analyzing time series of water chemical data from 71 lakes, 30 streams, and 4 river mouths distributed across all of Sweden over a 17 year period, we observed significant DOC concentration increases in 39 lakes, 15 streams, and 4 river mouths. Significant pCO2 increases were, however, only observed in six of these 58 waters, indicating that long‐term DOC increases in Swedish waters are disconnected from temporal pCO2 trends. We suggest that the uncoupling of trends in DOC concentration and pCO2 are a result of increased surface water runoff. When surface water runoff increases, there is likely less CO2 relative to DOC imported from soils into waters due to a changed balance between surface and groundwater flow. Additionally, increased surface water runoff causes faster water flushing through the landscape giving less time for in situ CO2 production in freshwaters. We conclude that pCO2 is presently not following DOC concentration trends, which has important implications for modeling future CO2 emissions from boreal waters.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".