Bibliographic record
Abstract
The purposes of this report are two-fold, i) documenting the expected impacts of climate change on Saskatchewan's water resources, ii) outlining the options for adaptation of water resource management practices, policies and infrastructure to minimize the risk associated with the impacts of climate change. Prairie province hydrology is dominated by cold regions processes so that snowmelt is the primary hydrological event of the year for both the major rivers that derive from the Rocky Mountains and small streams and rivers that arise in Saskatchewan. Climate change impacts on water resources are therefore focussed on changes to snow accumulation, snowmelt and infiltration to frozen soils. Climate change scenarios suggest generally warmer and wetter winters for Saskatchewan. Large scale hydrological models that take these scenarios into account suggest changes in the annual streamflow of the South Saskatchewan River ranging from an 8% increase to a 22 % decrease, with an 8.5 % decrease being an average prediction. Small scale hydrological models for prairie streams suggest a 24 % increase in spring runoff by 2050 followed by a 37 % decrease by 2080 is possible as the winter snowcover becomes discontinuous. Both model results suggest that there is not a dramatic drying of the prairies to be anticipated under
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.546 | 0.223 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".