Impact of climate variability and wetland drainage on watershed response in depression dominated landscapes
Bibliographic record
Abstract
This study investigates changes in run-off production behaviour which may have occurred due to climate change/variability and/or as a result of draining of wetlands over Canadian portion of North American Prairies. The study uses statistical methods to quantify changes in precipitation/run-off over various spatial and temporal scales. The major results indicate dominated upward trends in some run-off metrics over some Prairie watersheds, whereas there is no concrete evidence of statistically significant precipitation trends during the observation period. The observed changes in run-off response, therefore, are interpreted to represent the possible effects of intensive wetland drainage. The remaining unchanged metrics over the majority of tested watersheds are interpreted to be due to varying progressive land use/cover disturbances which may have conflicting impacts on watershed response in the Prairies. The absence of significant changes in precipitation and observed changes in hydrology of some parts of the study area may support the narrative that loss of wetlands has led to increased flood risks in this area. However, information on major land cover indices like intact forests, agricultural land, urban areas within the study area, and landscape best management practices would be necessary to fully comprehend land use change and its impact on the Prairie’s hydrology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".