Carbon and water cycling in lake‐rich landscapes: Landscape connections, lake hydrology, and biogeochemistry
Bibliographic record
Abstract
Lakes are low‐lying connectors of uplands and wetlands, surface water and groundwater, and though they are often studied as independent ecosystems, they function within complex landscapes. One such highly connected region is the Northern Highland Lake District (NHLD), where more than 7000 lakes and their watersheds cycle water and carbon through mixed forests, wetlands, and groundwater systems. Using a new spatially explicit simulation framework representing these coupled cycles, the Lake, Uplands, Wetlands Integrator (LUWI) model, we address basic regional questions in a 72‐lake simulation: (1) How do simulated water and carbon budgets compare with observations, and what are the implications for carbon stocks and fluxes? (2) How do the strength and spatial pattern of landscape connections vary among watersheds? (3) What is the role of interwatershed connections in lake carbon processing? Results closely coincide with observations at seasonal and annual scales and indicate that the connections among components and watersheds are critical to understanding the region. Carbon and water budgets vary widely, even among nearby lakes, and are not easily predictable using heuristics of lake or watershed size. Connections within and among watersheds exert a complex, varied influence on these processes: Whereas inorganic carbon budgets are strongly related to the number and nature of upstream connections, most organic lake carbon originates within the watershed surrounding each lake. This explicit incorporation of terrestrial and aquatic processes in surface and subsurface connection networks will aid our understanding of the relative roles of on‐land, in‐lake, and between‐lake processes in this lake‐rich region.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".