Development of VFDM: a riparian vegetated filter dimensioning model for agricultural watersheds
Bibliographic record
Abstract
Dimensioning and positioning structural beneficial management practices (BMPs) represent a “real life” challenge for soil conservation engineers, managers, planners and policy-makers. Different factors, such as trapping efficiency; implementation, management, and opportunity costs (resulting from cropland loss), and government policies and guidelines need to be weighed to meet this challenge. The trapping efficiency of structural BMPs may depend on many parameters, including: (1) characteristics of vegetated filters (VF) such as width and slope, vegetation height, vegetation density and species composition, (2) flow characteristics such as runoff velocity, discharge volume and water height, and (3) sediment characteristics such as particle size, aggregation and concentration. Government policies and guidelines may include dimension and location of VFs and/or a cropland percentage that needs to be converted into VF areas. The main objectives of this paper are to: (1) describe the development of the Vegetated Filter Dimensioning Model (VFDM), a mathematical model to determine the optimal dimensions of riparian vegetated filter strips (RVFSs) in agricultural watersheds, and (2) illustrate the potential use of the model on a pilot watershed, the Beaurivage watershed, in Quebec, Canada. The latter was done for the sole purpose of model testing with readily available input parameters and data. The model calculates the optimal width with respect to vegetation, topographical, hydrological and sedimentological characteristics. The results of this case study showed that the average recommended RVFS for the Beaurivage River watershed is about 3 m wide.Le dimensionnement et le positionnement de bandes riveraines (BR) représentent un défi important pour les ingénieurs, gestionnaires, planificateurs et les représentants politiques qui décident des régulations dans ce domaine. Plusieurs facteurs, tel que l’efficacité de captation des sédiments; l’implémentation, la gestion et les coûts d’opportunité (résultant de la perte de terres agricoles), et les politiques et régulations gouvernementales doivent être pris en compte pour relever ce défi. L’efficacité de captation dépend de plusieurs paramètres, incluant (1) les caractéristiques de la BR, tel que sa largeur et sa pente, la hauteur de la végétation, la densité de végétation et les espèces en présence; (2) les caractéristiques de l’écoulement, tel que la vitesse de ruissellement, le volume d’écoulement et la hauteur d’eau, et (3) les caractéristiques des sédiments, tel que la taille des particules, leur agrégation et leur concentration. Les politiques et régulations gouvernementales peuvent inclure la dimension et l’emplacement des BR et/ou un pourcentage de terres agricoles devant être converties en BR. Les objectifs principaux de cet article sont : (1) décrire le développement de VFDM (Vegetated Filter Dimensioning Model), un modèle mathématique pour déterminer les dimensions optimales des bandes riveraines dans des bassins versants agricoles, et (2) illustrer l’utilisation potentielle du modèle sur un bassin versant pilote, le bassin de la rivière Beaurivage, Québec, Canada. Ce dernier objectif a été fait dans le but de tester le modèle avec des paramètres et données d’entrées déjà disponibles. Le modèle calcule la largeur optimale en fonction des caractéristiques topographiques, hydrologiques, sédimentologiques et de la végétation. Les résultats de cette étude indiquent que les largeurs des bandes riveraines dans le bassin de la rivière Beaurivage devraient être de l’ordre de 3 m.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".