Landscape heterogeneity influences carbon dioxide production in a young boreal reservoir
Bibliographic record
Abstract
Surface carbon dioxide (CO2) emissions exhibit a high degree of spatial heterogeneity in the young boreal Eastmain-1 hydroelectric reservoir, located in northern Quebec, Canada. Estimates of the individual components of net CO2 production within the reservoir (benthic respiration, water column respiration, and primary production) furthermore provide a link between the heterogeneity in surface CO2 emissions and the flooded landscapes below. Specifically, the preflood carbon stock and soil–sediment respiration rates of flooded landscapes were found to influence benthic CO2 production, the rate of decline of hypolimnetic dissolved organic carbon (DOC), and the estimated rate at which flooded landscapes release DOC, further influencing water column respiration rates. Estimates of the individual components of net CO2 production in Eastmain-1 are supported by a positive relationship (t test, r2 = 0.64, P < 0.01) between measured surface CO2 emissions (mean ± SE = 1540 ± 145.4 mg C·m–2·day–1) and independently derived estimates of total net CO2 production (mean ± SE = 1230 ± 162.4 mg C·m–2·day–1). Our findings emphasize the utility of fundamental landscape characterization prior to construction in predicting reservoir greenhouse gas emissions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".