A coupled hydrology–biogeochemistry model to simulate dissolved organic carbon exports from a permafrost‐influenced catchment
Bibliographic record
Abstract
Abstract We outline the development of a simple, coupled hydrology–biogeochemistry model for simulating stream discharge and dissolved organic carbon (DOC) dynamics in data sparse, permafrost‐influenced catchments with large stores of soil organic carbon. The model incorporates the influence of active layer dynamics and slope aspect on hydrological flowpaths and resulting DOC mobilization. Calibration and evaluation of the model was undertaken using observations from Granger Basin within the Wolf Creek research basin, Yukon, northern Canada. Results show that the model was able to capture the dominant hydrological response and DOC dynamics of the catchment reasonably well. Simulated DOC was highly correlated with observed DOC (r2 = 0.65) for the study period. During the snowmelt period, the model adequately captured the observed dynamics, with simulations generally reflecting the timing and magnitude of the observed DOC and stream discharge. The model was less successful over the later summer period although this partly reflected a lack of DOC observations for calibration. The developed model offers a valuable framework for investigating the interactions between hydrological and DOC processes in these highly dynamic systems, where data acquisition is often very difficult. © 2015 The Authors Hydrological Processes Published by John Wiley & Sons, Ltd.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".