Patterns and regulation of dissolved organic carbon: An analysis of 7,500 widely distributed lakes
Bibliographic record
Abstract
Dissolved organic carbon (DOC) is a key parameter in lakes that can affect numerous features, including microbial metabolism, light climate, acidity, and primary production. In an attempt to understand the factors that regulate DOC in lakes, we assembled a large database (7,514 lakes from 6 continents) of DOC concentrations and other parameters that characterize the conditions in the lakes, the catchment, the soil, and the climate. DOC concentrations were in the range 0.1–332 mg L−1, and the median was 5.71 mg L−1. A partial least squares regression explained 48% of the variability in lake DOC and showed that altitude, mean annual runoff, and precipitation were negatively correlated with lake DOC, while conductivity, soil carbon density, and soil C:N ratio were positively related with lake DOC. A multiple linear regression using altitude, mean annual runoff, and soil carbon density as predictors explained 40% of the variability in lake DOC. While lake area and drainage ratio (catchment : lake area) were not correlated to lake DOC in the global data set, these two factors explained significant variation of the residuals of the multiple linear regression model in several regional subsets of data. These results suggest a hierarchical regulation of DOC in lakes, where climatic and topographic characteristics set the possible range of DOC concentrations of a certain region, and catchment and lake properties then regulate the DOC concentration in each individual lake.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".