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 ( CO 2 ) and methane ( CH 4 ) diffusive emissions were measured during two field surveys in Q ueensland and T asmania, A ustralia, using the floating chamber method. Bubbling and degassing emissions in 2010 were estimated in K oombooloomba D am 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 Q ueensland and temperate T asmania, and between the humid W est C oast R ange ( T asmania) and dry C entral P lateau ( T asmania). Higher CO 2 and CH 4 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 K oombooloomba D am reservoir were about 1.5 × 10 6 t CO 2 eq km 2 per year, or 24 × 10 6 t CO 2 eq per year. This corresponds to a plant emission factor of 3.18 kg CO 2 eq MW h −1 . Using an estimate of terrestrial emissions derived from literature data for the T ully R iver catchment area, rough estimated net emissions from the catchment area are about 44 kt CO 2 eq per year, or 5.83 kg CO 2 eq MW h −1 , which is in the lower range of the studied reservoirs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".