Comparison of Hydrologic Dynamics in Forested and Agricultural Sub-watersheds of a Large Mixed-use Prairie Watershed
Bibliographic record
Abstract
The natural history of the Prairies includes the large-scale human modification of landscape biology and hydrology from first settlement to present. Forested land has been and continues to be lost and runoff is increasingly artificially drained in this intensively managed region. The impact of such modifications on hydrological dynamics has yet to be understood in such a way that measurable landscape alterations (i.e., area of forest lost, hydraulic capacity of artificial surface drains) can be linked to quantifiable alterations in event storm hydrographs or hydrological regimes. Here we focused on a large mixed-used watershed to compare the temporal hydrological dynamics of forested sub-watersheds to those of neighbouring, deforested agricultural sub-watersheds within a similar geologic and pedologic setting. The Catfish Creek Watershed (CCW) drains a 600 km2 area located approximately 90 km north-east of Winnipeg (Manitoba, Canada; see Fig. 1a) and has been extensively impacted by human activities including the continued clearing of forested land for cultivation. It is characterized as a low-relief, agro-forested watershed (~45% forest, ~40% crops, ~10% swamp, ~5% other, see Fig. 1b). Surface runoff is managed in part by a network of artificial drains in both the forested and cultivated portions of this watershed. The lower CCW is naturally-vegetated by parkland forest and swamp. The eastern edge of the upper watershed is also forested and of greater relative relief, while to the west the landscape is dominated by intensive, large-scale agricultural operations on a near-level landscape.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".