The relative influence of topography and land cover on inorganic and organic carbon exports from catchments in southern Quebec, Canada
Bibliographic record
Abstract
Abstract Export of carbon (C) from watersheds represents a key component of local and regional C budgets. We explored the magnitude, variability, and drivers of inorganic, organic, and total C exports from 83 temperate catchments in southern Québec, Canada. The average dissolved inorganic carbon (DIC), dissolved organic carbon (DOC), and total C (TC) exports from these catchments were 4.6, 5.1, and 10.2 g m −2 yr −1 , respectively. Multiple regression models, using a combination of topographical variables (catchment area, shape, and slope), along with land cover variables (%vegetation, %wetland, %lake, and building density), explained 34%, 62%, and 53% of the variability in the DIC, DOC, and TC exports, respectively. Variance partitioning in the models revealed that topography is slightly more important than land cover in explaining the variance in DIC export (19% versus 15%), whereas land cover is much more important than topography in determining DOC export (44% versus 18%). Interestingly, %vegetation had a negative effect on DIC export but a positive effect on DOC export, suggesting that a change in land cover that reduces vegetation (e.g., deforestation) would lead to modest decreases in TC export but large increases in DIC/DOC export ratio. We conclude that topography and land cover together determine DIC, DOC, and TC exports. While topography is static, land cover can be altered, which will determine the quantity, form, and fate of C exported from these catchments. Finally, annual differences in export values that are related to temperature and precipitation suggest that climate change also have an impact on C export.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".