A coupled two-dimensional hydrodynamic and terrestrial input model to simulate CO<sub>2</sub> diffusive emissions from lake systems
Bibliographic record
Abstract
Abstract. Most lakes worldwide are supersaturated with carbon dioxide (CO2) and consequently act as atmospheric net sources. Since CO2 is a major greenhouse gas (GHG), the accurate estimation of CO2 exchanges at air/water interfaces of aquatic ecosystems is vital in quantifying the carbon budget of aquatic ecosystems overall. To date, lacustrine CO2 emissions are poorly understood, and lake carbon source proportions remain controversial, largely due to a lack of integration between aquatic and terrestrial ecosystems. In this paper a new process-based model (TRIPLEX-Aquatic) is introduced incorporating both terrestrial inputs and aquatic biogeochemical processes to estimate diffusive emissions of CO2 from lake systems. The model was built from a two-dimensional hydrological and water quality model coupled with a new lacustrine CO2 diffusive flux model. For calibration and validation purposes, two years of data collected in the field from two small boreal oligotrophic lakes located in Québec (Canada) were used to parameterize and test the model by comparing simulations with observations for both hydrodynamic and carbon process accuracy. Model simulations were accordant with field measurements in both calibration and verification. Consequently, the TRIPLEX-Aquatic model was used to estimate the annual mean CO2 diffusive flux and predict terrestrial dissolved organic carbon (DOC) impacts on the CO2 budget for both lakes. Results show a significant fraction of the CO2 diffusive flux (~30–45%) from lakes was primarily attributable to the input and mineralization of terrestrial DOC, which indicated terrestrial organic matter was the key player in the diffusive flux of CO2 from oligotropical lake systems in Québec, Canada.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".