Applying the Soil Water Assessment Tool to 5th Canadian Division Support Base Gagetown
Bibliographic record
Abstract
A hydrologic and water quality model is sought to establish an approach to land management decisions for a Canadian Army training base. Training areas are subjected to high levels of persistent activity creating unique land cover and land-use disturbances. Deforestation, complex road networks, off-road manoeuvres, and vehicle stream crossings are among major anthropogenic activities observed to affect these landscapes. Expanding, preserving and improving the quality of these areas to host training activities for future generations is critical to maintain operational effectiveness. Inclusive to this objective is minimizing resultant environmental degradation, principally in the form of hydrologic fluctuations, excess erosion, and sedimentation of aquatic environments. Application of the Soil Water Assessment Tool (SWAT) was assessed for its ability to simulate hydrologic and water quality conditions observed in military landscapes at 5th Canadian Division Support Base (5 CDSB) Gagetown, New Brunswick. Despite some limitations, this model adequately simulated three partial years of daily watershed outflow (NSE = 0.47–0.79, R2 = 0.50–0.88) and adequately predicted suspended sediment yields during the observation periods (%d = 6–47%) for one highly disturbed sub-watershed in Gagetown. Further development of this model may help guide decisions to develop or decommission training areas, guide land management practices and prioritize select landscape mitigation efforts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".