Long-term trends in carbon and color signal uneven browning and terrestrialization of northern lakes
Bibliographic record
Abstract
Abstract The widespread browning of northern lakes has been associated with long-term increases in dissolved organic carbon and color and should be linked to changes in surface water carbon dioxide, yet the long-term covariation in these three key carbon components of lake functioning remains to be assessed. We present long-term trends in dissolved organic carbon, color, and carbon dioxide from lakes, with generally positive but highly variable trends in organic carbon and a large degree of uncoupling with color and carbon dioxide. The highest rates of change in color and carbon dioxide were in lakes with greatest increasing dissolved organic carbon trends. Lakes with the lowest water retention times had greater increases and stronger coupling between all three parameters, coinciding with dominance of terrestrially derived carbon. These results suggest an uneven terrestrialization of northern lakes, where the increases and coupling in the three carbon components depends on hydrology and watershed connectivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".