A century of human‐driven changes in the carbon dioxide concentration of lakes
Bibliographic record
Abstract
Abstract Now that evasion of carbon dioxide (CO2) from inland waters is accounted for in global carbon models, it is crucial to quantify how these fluxes have changed in the past and forecast how they may alter in the future in response to local and global change. Here we developed a sediment proxy for the concentration of summer surface dissolved CO2 concentration and used it to reconstruct changes over the past 150 years for three large lakes that have been affected by climate warming, changes in nutrient load, and detrital terrigenous supplies. Initially CO2 neutral to the atmosphere, all three lakes subsequently fluctuated between near equilibrium and supersaturation. Although catchment inputs have supplied CO2 to the lakes, internal processes and reallocation have ultimately regulated decadal changes in lake surface CO2 concentration. Nutrient concentration has been the dominant driver of CO2 variability for a century although the reproducible, nonmonotonic relationship of CO2 to nutrient concentration suggests an interplay between metabolic and chemical processes. Yet for two of these lakes, climatic control of CO2 concentrations has been important over the last 30 years, promoting higher surface CO2 concentrations, likely by decreasing hypolimnetic carbon storage. This new approach offers the unique opportunity to scale, a posteriori, the long‐term impact of human activities on lake CO2.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".