Assessing the transport of total phosphorus from a prairie river basin using SPARROW
Bibliographic record
Abstract
Abstract Recent changes in land use and new industrial developments in river basins around the world have raised serious concerns regarding future water quality. Assessment of regional nutrient export from river basins are needed in order to identify main nutrient sources and hydrological variables involved. The Red Deer River catchment in Alberta, Canada has been chosen as a test case to assess total phosphorus export regionally. Agricultural and livestock activities in the catchment have increased manure production and the supply of fertilizer to crops. Oil and gas exploitation has also increased the risk of surface water and groundwater contamination. The rapid population growth has not only lead to increases in water consumption and wastewater discharges but also to further construction of transportation infrastructure and the expansion of new urban developments. This has imposed hydraulic controls on waterways, affecting the catchment hydrology and changing the dynamics of sediment and nutrient transport. River ecosystems are not exempt from the negative effects of water quality deterioration. Downstream from the city of Red Deer, the physiology and reproduction habits of native fish species have changed because of high riverine productivity. Although improvements have been made to surface water quality standards by Alberta Environment, further research is needed in order to identify major nutrient sources and quantify nutrient export. The SPAtially Referenced Regression On Watershed model has therefore been used in this study of the Red Deer River catchment to assess regional water quality, in order to describe the spatial and temporal patterns of the processes that affect water quality. The model is suitable for interpreting monitoring data sets that suffer from network sparseness, bias and basin heterogeneity. Ultimately, the model could provide improved information to environmental agencies to guide future water quality management practices and policies. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 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".