Greenhouse Gas Emissions from Boreal Reservoirs in Manitoba and Québec, Canada, Measured with Automated Systems
Bibliographic record
Abstract
Growing concern over the contribution of freshwater reservoirs to increases in atmospheric greenhouse gas (GHG) concentrations and the relevance of long-term continuous measurements has led Fisheries and Oceans Canada, in conjunction with Manitoba Hydro, to develop continuous GHG monitors. Continuous water pCO(2), pCH(4), and pO(2) measurements were gathered to estimate gas fluxes in one temperate reservoir (Riviere-des-Prairies) and two boreal reservoirs (Eastmain-1 and Robert-Bourassa) in Quebec, and in four boreal reservoirs (Grand Rapids, Jenpeg, Kettle, and McArthur Falls) in Manitoba, Canada. Mean daily CO(2) fluxes ranged between 7 and 14 mmolCO(2)*m(-2)*d(-1) in Manitoba and between 15 and 55 mmolCO(2)*m(-2)*d(-1) in Quebec. Summertime episodes of water undersaturation in CO(2) were observed at Jenpeg, Kettle, and McArthur, suggesting higher productivities of these systems compared to the other systems studied. Mean daily CH(4) fluxes ranged between 0 and 69 micromolCH(4)*m(-2)*d(-1) in Manitoba and between 9 and 48 micromolCH(4)*m(-2)*d(-1) in Quebec. Comparisons of results obtained in the Eastmain-1 area using automated monitors, floating chambers or dissolved gas analyses over multiple-station field campaigns demonstrated that a continuous GHG monitor at a single sampling station provided representative and robust results.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".