Ecosystem Metabolism Is the Dominant Source of Carbon Dioxide in Three Young Boreal Cascade‐Reservoirs (La Romaine Complex, Québec)
Bibliographic record
Abstract
Abstract The impoundment of rivers for multipurpose reservoirs has significant consequences to the carbon cycle, one of the most relevant being the increase in greenhouse gases emissions. Reservoirs have been shown to be net sources of such gases to the atmosphere, emitting between 0.8 and 1.08 Pg carbon dioxide (CO 2 ) equivalents per year. Even though emission estimates have become common, less is known about the processes driving this CO 2 excess, a prerequisite for understanding and ultimately predicting and managing CO 2 emissions from reservoirs. In the present study, we aimed at exploring ecosystem metabolism (gross primary production, ecosystem respiration, and net ecosystem production [NEP]) and its environmental drivers in three young hydroelectric reservoirs in a cascade configuration but with distinctive morphometries (shape, depth, and size). By combining our metabolic measurements with a hydrological mass balance approach, we quantified the relative contributions of internal (ecosystem metabolism) versus external sources (tributaries and groundwater) to the reservoir surface diffusive CO 2 emissions. There was a predominance of net heterotrophy in all sites, and metabolism played a key role in fueling CO 2 fluxes in all three reservoirs, NEP alone being able to account for the measured fluxes in approximately 50% of all sites. Internal production was thus the main process explaining total reservoir CO 2 diffusive emissions (∼100%), with groundwater and tributaries contributing similarly but more modestly (∼9% each). Our results contribute to our understanding of the processes underlying boreal reservoir carbon footprints, and in particular, for apportioning the emissions can be attributed to the reservoir itself.
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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 | 0.001 |
| 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".