Integrating aquatic and terrestrial components to construct a complete carbon budget for a north temperate lake district
Bibliographic record
Abstract
Abstract The development of complete regional carbon (C) budgets for different biomes is an integral step in the effort to predict global response and potential feedbacks to a changing climate regime. Wetland and lake contributions to regional C cycling remain relatively uncertain despite recent research highlighting their importance. Using a combination of field surveys and tower‐based carbon dioxide (CO2) flux measurements, modeling, and published literature, we constructed a complete C budget for the Northern Highlands Lake District in northern Wisconsin/Michigan, a ∼6400 km2region rich in lakes and wetlands. This is one of the first regional C budgets to incorporate aquatic and terrestrial C cycling under the same framework. We divided the landscape into three major compartments (forests, wetlands, and surface waters) and quantified all major C fluxes into and out of those compartments, with a particular focus on atmospheric exchange but also including sedimentation in lakes and hydrologic fluxes. Landscape C storage was dominated by peat‐containing wetlands and lake sediments, which make up only 20% and 13% of the landscape area, respectively, but contain >80% of the total fixed C pool (ca. 400 Tg). We estimated a current regional C accumulation of 1.1±0.1 Tg yr−1, and the largest regional flux was forest net ecosystem exchange (NEE) into aggrading forests for a total of 1.0±0.1 Tg yr−1. Mean wetland NEE (0.12±0.06 Tg yr−1into wetlands), lake CO2emissions and riverine efflux (each ca. 0.03±0.01 Tg yr−1) were smaller but of consequence to the overall budget. Hydrologic transport from uplands/wetlands to surface waters within the region was an important vector of terrestrial C. Regional C fluxes and pools would be misrepresented without inclusion of surface waters and wetlands, and C budgets in heterogeneous landscapes open opportunities to examine the sensitivities of important fluxes to changes in climate and land use/land cover.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".