The effects of climate change on nutrient loading and river discharge
Bibliographic record
Abstract
This study was conducted to identify temporal changes in nutrient and sediment concentrations and loads (total phosphorus, particulate phosphorus, total dissolved phosphorus, total nitrogen, and total suspended solids) in Swan River and Woody River of the Swan Lake watershed, Manitoba. Temporal changes in physical hydrology (river discharge and precipitation) were also investigated to determine if these parameters influenced the changes in water quality concentrations and loads across the Swan Lake watershed. Annual and seasonal totals of water quality variables, river discharge, and average watershed total precipitation were examined for change over 30 years. The results showed a statistically significant increase in nutrients and total suspended solids (TSS), and river discharge, particularly in Swan River. Both rivers experienced statistically significant increases during the spring season with changes in median values as high as 450% in TSS between 1989 – 2000 and 2010 – 2018. Annual river discharge in Swan River and Woody River increased by 182% and 103%, respectively, with Swan River experiencing a statistically significant increase over the 30-year period. Seasonally, both rivers increased statistically significantly in the spring season with an 80% increase. Total precipitation across the watershed increased 3% annually, including a 6% increase in the spring, and summer and fall seasons, and 8% decrease in the winter season between 1995 – 2001 and 2009 – 2015. There were correlations between water quality variables and river discharge, and between river discharge and precipitation. Precipitation in this area influences river discharge and since nutrients and sediments are strongly correlated with river discharge, precipitation indirectly influences nutrient and sediment exports.
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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) | 0.000 | 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) | 0.001 | 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".