Impacts of Climate Change on the Water Quality of a Regulated Prairie River
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Flows along the upper Qu’Appelle River are expected to increase in the future via increased discharge from Lake Diefenbaker to meet the demands of increased agricultural and industrial activity and population growth in southern Saskatchewan. This increased discharge and increased air temperature due to climate change are both expected to have an impact on the water quality of the river. The Water Quality Analysis Simulation Program (WASP7) was used to model current and future water quality of the upper Qu’Appelle River. The model was calibrated and validated to characterize the current state of the water quality of the river. The model was then used to predict water quality [nutrient (nitrogen and phosphorus) concentrations and oxygen dynamics] for the years 2050–2055 and 2080–2085. The modelling results indicate that global warming will result in a decrease in ice thickness, a shorter ice cover period, and decreased nutrient concentrations in 2050 or 2080 relative to 2010, with a greater decrease of nutrient concentrations in open water. In contrast to the effect of warmer water temperatures, increased flow through water management may cause increases in ammonium, nitrate, and dissolved oxygen concentrations and decreases in orthophosphate concentrations in summer.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it