Modeling dissolved organic carbon mass balances for lakes of the Muskoka River Watershed
Bibliographic record
Abstract
Changes in the flux of dissolved organic carbon (DOC) into and out of lakes are important to the biogeochemistry of aquatic environments. The ability to estimate or model DOC fluxes and concentrations in lakes and other surface waters is of great benefit for investigations of aquatic systems. Spatial attributes of catchments were derived using GIS techniques and combined with published DOC mass balance models from 20 small study catchments and seven lakes to estimate DOC concentrations for hydrologically connected lakes (i.e. connected by surface or ground waters) of the Muskoka River Watershed, a large tertiary watershed (904 lakes) in southern Ontario. Predicted DOC concentrations were very dependent on the method used to estimate wetland area. When a Rapid Assessment Technique (RAT) was used to estimate wetland area, predicted and observed DOC concentrations were linearly related. Most of the DOC residuals were < 1 mg L−1. Inclusion of riparian wetlands or small lakes in the contributing catchments resulted in a slight improvement of model predictions, but not beyond the variability of the model. Model predictions of DOC were reasonable (according to model fit and residuals), especially considering it was a regional-scale study, but substantial variability was still unaccounted for. Applying the model to other regions with similar landscapes (i.e. other watersheds on the Precambrian Shield in North America and Nordic countries) is feasible.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".