Influence of hydro-morphologic variables of forested catchments on the increase in DOC concentration in 36 temperate lakes of eastern Canada
Bibliographic record
Abstract
In the last decades, a worldwide increase in dissolved organic carbon (DOC) concentrations has been observed in temperate and boreal lakes. This phenomenon has several detrimental effects on the aquatic life and affect local C geochemical cycles . In this study, we measured DOC concentration in the water column of 36 lakes located in eastern Canada over a period of 35 years (1983–2017) and assessed the influence of climatic, hydrologic and morphometric variables on both DOC concentrations and on the rate of DOC changes (∆DOC). Our data show that morphometric and hydrologic variables have a stronger direct influence on lake water DOC concentrations than vegetation and climatic variables. DOC concentration strongly increased with the drainage ratio and the surface covered by organic deposits, which together explained 59% of the variance. As expected, we observed a significant increase in lake water DOC concentration in 72% of the surveyed lakes, which averaged 20% over the study period. Meanwhile, lake water SO 4 2− concentration decreased by 60%. ∆DOC was poorly influenced by the rate of changes in lake water SO 4 2− as well as by the rate of changes in mean annual air temperature and precipitation. ∆DOC was more related to the vegetation type and the morphometry of the catchment: a model including the percentage of conifers, terrestrial catchment area and ∆Cl yielded a variance explanation of 39%. This shows that the rate of increase was primarily driven by morphometric variables which did not change over the study period.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".