The net carbon footprint of a newly created boreal hydroelectric reservoir
Bibliographic record
Abstract
We present here the first comprehensive assessment of the carbon (C) footprint associated with the creation of a boreal hydroelectric reservoir (Eastmain‐1 in northern Québec, Canada). This is the result of a large‐scale, interdisciplinary study that spanned over a 7‐years period (2003–2009), where we quantified the major C gas (CO2 and CH4) sources and sinks of the terrestrial and aquatic components of the pre‐flood landscape, and also for the reservoir following the impoundment in 2006. The pre‐flood landscape was roughly neutral in terms of C, and the balance between pre‐ and post‐flood C sources/sinks indicates that the reservoir was initially (first year post‐flood in 2006) a large net source of CO2 (2270 mg C m−2 d−1) but a much smaller source of CH4 (0.2 mg C m−2 d−1). While net CO2 emissions declined steeply in subsequent years (down to 835 mg C m−2 d−1 in 2009), net CH4 emissions remained constant or increased slightly relative to pre‐flood emissions. Our results also suggest that the reservoir will continue to emit carbon gas over the long‐term at rates exceeding the carbon footprint of the pre‐flood landscape, although the sources of C supporting these emissions have yet to be determined. Extrapolation of these empirical trends over the projected life span (100 years) of the reservoir yields integrated long‐term net C emissions per energy generation well below the range of the natural‐gas combined‐cycle, which is considered the current industry standard.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".