Uncertainty assessment of forest road modeling with the Distributed Hydrology Soil Vegetation Model (DHSVM)
Bibliographic record
Abstract
We used a generalized likelihood uncertainty estimation procedure with the Distributed Hydrology Soil Vegetation Model (DHSVM) for two streamflow and 11 road ditchflow locations. We observed considerable uncertainty in DHSVM simulations of forest road and stream runoff. The accuracy of simulations decreased as the size of the area modeled decreased. For streamflow, 44% of attempted model structures exceeded a 0.5 Nash–Sutcliffe efficiency threshold for a 630 ha catchment; 12% of attempted model structures exceeded a 0.5 Nash–Sutcliffe efficiency threshold for a 55 ha catchment. DHSVM simulations produced behavioral model structures for only six of the 11 road ditchflow sites (<10 ha). Cumulative distribution functions of parameter values did not indicate specific parameter ranges of parameter values across all locations, indicating that parameter values in DHSVM are influenced by their interaction with other parameters. The sensitivity of parameters and the range of that sensitivity varied across simulations of road ditchflow and streamflow. DHSVM simulations for two streamflow locations varied outside the uncertainty bounds for 10%–22% of storm volumes and 12%–22% of peak flows, respectively. Twenty-eight percent to 52% of storm volumes and 28%–48% of peak flows were outside the uncertainty bounds for the six road ditchflow locations.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".