Sources and fate of terrestrial dissolved organic carbon in lakes of a Boreal Plains region recently affected by wildfire
Bibliographic record
Abstract
Abstract. Downstream mineralization and sedimentation of terrestrial dissolved organic carbon (DOC) render lakes important for landscape carbon cycling in the boreal region, with regulating processes potentially sensitive to perturbations associated with climate change including increased occurrence of wildfire. In this study we assessed chemical composition and reactivity (during both dark and UV incubations) of DOC from lakes and terrestrial sources within a peatland-rich western boreal plains region partially affected by a recent wildfire. While wildfire was found to increase aromaticity of DOC in peat pore-water above the water table, it had no effect on concentrations or composition of DOC from peatland wells and neither affected mineral well or lake DOC characteristics. Lake DOC composition reflected a mixing of peatland and mineral groundwater, with a greater influence of mineral sources to lakes in coarse- than fine-textured settings. Peatland DOC was less biodegradable than mineral DOC, but both mineralization and sedimentation of peatland DOC increased substantially during UV incubations through selective removal of aromatic humic and fulvic acids. DOC composition in lakes with longer residence times had characteristics consistent with increased UV-mediated processing. We estimate that about half of terrestrial DOC inputs had been lost within lakes, mostly due to UV-mediated processes. The importance of within-lake losses of aromatic DOC from peatland sources through UV-mediated processes indicate that terrestrial-aquatic C linkages in the study region are largely disconnected from recent terrestrial primary productivity. Together, our results suggest that characteristics of the study region (climate, surface geology and lake morphometry) render linkages between terrestrial and aquatic C cycling insensitive to the effects of wildfire by determining dominant terrestrial sources and within-lake processes of DOC removal.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 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".