Spatio‐temporal variation of gross <scp><scp>CO<sub>2</sub></scp></scp> and <scp><scp>CH<sub>4</sub></scp></scp> diffusive emissions from <scp>A</scp>ustralian reservoirs and natural aquatic ecosystems, and estimation of net reservoir emissions
Bibliographic record
Abstract
Abstract Carbon dioxide (CO2) and methane (CH4) diffusive emissions were measured during two field surveys in Queensland and Tasmania, Australia, using the floating chamber method. Bubbling and degassing emissions in 2010 were estimated in Koombooloomba Dam reservoir using only inverted funnels and gas concentrations, respectively. A total of 14 reservoirs and 16 rivers and lakes were sampled from 2006 to 2010. Spatial variation was substantial within each water body, as well as between them. The main drivers of diffusive emission variation were physiographic region and climate, with a clear demarcation being observed between diffusive emissions from tropical Queensland and temperate Tasmania, and between the humid West Coast Range (Tasmania) and dry Central Plateau (Tasmania). Higher CO2 and CH4 diffusive emissions were observed during the dry season, when long water residence times would promote organic matter degradation. Estimated total gross emissions, including diffusive, bubbling and degassing emissions, for Koombooloomba Dam reservoir were about 1.5 × 106 t CO2eq km2 per year, or 24 × 106 t CO2eq per year. This corresponds to a plant emission factor of 3.18 kg CO2eq MWh−1. Using an estimate of terrestrial emissions derived from literature data for the Tully River catchment area, rough estimated net emissions from the catchment area are about 44 kt CO2eq per year, or 5.83 kg CO2eq MWh−1, which is in the lower range of the studied reservoirs.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".