Regional contribution of CO<sub>2</sub> and CH<sub>4</sub> fluxes from the fluvial network in a lowland boreal landscape of Québec
Bibliographic record
Abstract
Abstract Boreal rivers and streams are known as hot spots of CO 2 emissions, yet their contribution to CH 4 emissions has traditionally been assumed to be negligible, due to the spatially fragmented data and lack of regional studies addressing both gases simultaneously. Here we explore the regional patterns in river CO 2 and CH 4 concentrations ( p CO 2 and p CH 4 ), gas exchange coefficient ( k ), and the resulting emissions in a lowland boreal region of Northern Québec. Rivers and streams were systematically supersaturated in both gases, with both p CO 2 and p CH 4 declining along the river continuum. The k was on average low and increased with stream order, consistent with the hydrology of this flat landscape. The smallest streams (order 1), which represent < 20% of the total river surface, contributed over 35% of the total fluvial greenhouse gas (GHG) emissions. The end of winter and the spring thaw periods, which are rarely included in annual emission budgets, contributed on average 21% of the annual GHG emissions. As a whole, the fluvial network acted as significant source of both CO 2 and CH 4 , releasing on average 1.5 tons of C (CO 2 eq) yr −1 km −2 of landscape, of which CH 4 emissions contributed approximately 34%. We estimate that fluvial CH 4 emissions represent 41% of the regional aquatic (lakes, reservoirs, and rivers) CH 4 emissions, despite the relatively small riverine surface (4.3% of the total aquatic surface). We conclude that these fluvial networks in boreal lowlands play a disproportionately large role as hot spots for CO 2 and more unexpectedly for CH 4 emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.001 |
| 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 teacher head, 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".