Diel variation of CO2 concentrations is substantial in Australian irrigation dams
Bibliographic record
Abstract
[1]¿p#1 Small artificial waterbodies such as farm dams are recognised as important sources of anthropogenic emissions. Carbon dioxide (CO2) fluxes from farm dams are highly variable across spatial scales, with sites acting as sources or sinks based on single daytime measurements. However, diel variations are an overlooked source of temporal variability in CO2, complicating upscaling efforts. To provide a more accurate estimate of the CO2 source-sink status of farm dams, four irrigation dams were monitored for dissolved CO2 at half-hourly intervals over a five-month period in 2021-2022. Carbon dioxide concentrations were undersaturated between 12% to 37% of the measurements, indicating a dominance of CO2 production over consumption when considering diel cycles and seasonal patterns. Carbon dioxide was consistently lower during daytime and early night periods compared with late-night and exhibited large diel shifts up to 57-99 µM. Diel CO2 amplitude was driven primarily by metabolic controls, as revealed by strong correlations with dissolved oxygen and temperature, except for one irrigation dam which indicated CO2 variation was driven by external inputs. Spatial variability based on previously acquired data (inter-dam coefficient of variation (CV) 105%) was higher than diel (CV 34-54%) and seasonal (CV 13-29%) variability. Yet if only daytime CO2 measurements are considered, total CO2 emissions over the study period would be underestimated by between 11% to 148%, demonstrating the consequence of not including diel variation. Future refinements of farm dam CO2 emission estimates will require more geographically dispersed studies that feature diel measurements to constrain uncertainty in these highly variable systems.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".