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 CO2 emissions, yet their contribution to CH4 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 CO2 and CH4 concentrations (pCO2 and pCH4), 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 pCO2 and pCH4 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 CO2 and CH4, releasing on average 1.5 tons of C (CO2 eq) yr−1 km−2 of landscape, of which CH4 emissions contributed approximately 34%. We estimate that fluvial CH4 emissions represent 41% of the regional aquatic (lakes, reservoirs, and rivers) CH4 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 CO2 and more unexpectedly for CH4 emissions.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".