A Literature Review of Nutrient Management-Related Best management Practices used in the Lake Winnipeg Basin
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,013 | 0,017 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».