Wildfires mediate carbon transfer from land to lakes across boreal and temperate regions
Bibliographic record
Abstract
Wildfires can disrupt carbon transport from land to water, but how lake carbon cycling responds to fires remains unclear. Here, we analyzed the concentration and dominance of the main carbon forms in total carbon pools in 54 lakes (34 burned, 20 control) across 3 regions of Quebec, Canada and Minnesota, USA from recent wildfires ( < 1 – 3 years). Lakes in burned watersheds had up to double the dissolved organic carbon concentrations of control lakes, and the fire effect was most apparent when accounting for climate and landscape drivers (e.g., catchment to lake area ratio) of lake carbon cycling. The greater quantity and dominance of dissolved organic carbon in burned lakes over other carbon forms with different turnover rates and fates suggest a potential fire-mediated carbon export up to several years post fire with a yet undetermined fate in northern forested watersheds and with important implications for regional to global carbon budgets. Wildfires can increase lake carbon concentrations by up to double, mostly in the form of dissolved organic carbon, according to analyses of carbon in fire-disturbed and control lakes across Quebec and Minnesota.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".