A SPATIALLY EXPLICIT WATERSHED‐SCALE ANALYSIS OF DISSOLVED ORGANIC CARBON IN ADIRONDACK LAKES
Bibliographic record
Abstract
Terrestrial ecosystems contribute significant amounts of dissolved organic carbon (DOC) to aquatic ecosystems. Temperate lakes vary in DOC concentration as a result of variation in the spatial configuration and composition of vegetation within the watershed, hydrology, and within‐lake processes. We have developed and parameterized a spatially explicit model of lake DOC concentrations, using data from 428 watersheds in the Adirondack Park of New York. Our analysis estimates watershed loading to each lake as a function of the cover type of each 10 × 10 m grid cell within the watershed, and its flow‐path distance to the lake. The estimated export rates for the three main forest cover types were 37.7–47.0 kg C·ha−1·yr−1. The four main wetland cover types had much higher rates of export per unit area (188.4–227.0 kg C·ha−1·yr−1), but wetlands occupied only 11%, on average, of watershed area. As a result, upland forests were the source of ∼70% of DOC loading. There was evidence of significant interannual variation in DOC loading, correlated with interannual variation in precipitation. Estimated net in situ DOC production within the lakes was extremely low (<1 kg C·ha−1·yr−1). Many of the lakes have large watersheds relative to lake volume and have correspondingly high flushing rates. As a result, losses due to lake discharge generally had a larger effect on lake DOC concentrations than in‐lake decay. Our approach can be readily incorporated within a GIS framework and allows examination of scenarios such as loss of wetlands, alterations in forest management, or increases in conserved areas, as a function of the unique configuration of individual watersheds.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".