A Literature Review of Nutrient Management-Related Best management Practices used in the Lake Winnipeg Basin
Bibliographic record
Abstract
This literature review aims to provide a resource for quantifying reductions in phosphorus (P) and nitrogen (N) that contribute to nutrient pollution in Lake Winnipeg. Water quality has deteriorated in the lake due to multiple sources of excessive nutrients in the watershed that have increased the frequency and magnitude of algal blooms, including toxic Cyanobacteria or blue-green algae blooms. This has both direct and indirect consequences on not only the biological health of Lake Winnipeg, but has economic, recreation and tourism repercussions as well. Targets have been set to restore conditions that are similar to those from the 1990s, before occurrences of algal blooms had doubled in frequency and size (Schindler, Hecky & McCullough, 2012). Optimal targets of annual total P and N concentrations of Lake Winnipeg have been set as 4,850 and 62,140 tonnes, respectively (Manitoba Conservation and Water Stewardship, 2015). As the tenth largest freshwater lake in the world, Lake Winnipeg has a watershed area of almost one million km2, spanning over four Canadian provinces and four American states (Wassenaar & Rao, 2012). To achieve the target concentrations, ten percent reductions in both P and N loading in the Winnipeg, Saskatchewan and Dauphin River watersheds is required, with a fifty percent reduction in P loading and a thirty percent reduction in N loading in the Red River watershed (Manitoba Conservation and Water Stewardship, 2015). It will require multi-jurisdictional cooperation and involvement to achieve such targets. The Lake Winnipeg Basin Initiative (LWBI) is the Government of Canada’s response to address nutrient pollution issues in Lake Winnipeg. The LWBI aims to engage citizens, scientists and domestic and international partners in actions to restore the ecological health of Lake Winnipeg, reduce nutrient pollution and improve water quality. The LWBI provides support for high-impact, stakeholder-based projects that improve the health of the watershed through the Lake Winnipeg Basin Stewardship Fund (LWBSF). Examples of projects funded by the LWBSF include: implementation of Best Management Practices (BMPs) that reduce rural or urban non-point sources of nutrients (e.g. water retention projects, conservation tillage, riparian enhancements, nutrient management and recovery projects); wetland conservation and restoration; development of innovative technologies that reduce nutrient loading from municipal wastewater systems or other point source discharges; cattle fencing and alternative watering systems. The LWBSF requires successful applicants to provide information as to how effective their project was in reducing nutrients. Ideally that information is based on water quality monitoring of nutrient concentrations conducted before and after implementation of their project. Unfortunately for many stakeholder-led projects water quality data is not produced due to the lack of capacity to conduct such monitoring. In those cases, nutrient reductions are estimated based on values derived from the scientific literature. In the past, estimates derived from the Phosphorus Reduction Calculation Report developed by Environment Canada’s Lake Simcoe Clean-up Fund were used in the Lake Winnipeg program (Sealock, 2011). The objective of this report is to provide nutrient reduction estimates based on research conducted primarily in the Lake Winnipeg Basin (LWB) that more accurately reflects the hydroclimatic and soil conditions and then can be used to help quantify the impact of the implementation of projects funded by the LWBSF on nutrient loading in the LWB.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".