Suspended sediment modelling in the Nerepis River system.
Bibliographic record
Abstract
L'érosion causée par les zones d'entraînement militaire peut apporter des quantités considérables de sédiments dans les cours d'eau à proximité. (Soil Water Assessment Tool, ou SWAT) a été sélectionné comme outil de modélisation pour sa capacité à simuler les conditions hydrologiques et la qualité de l'eau observées dans les paysages militaires de la Base de soutien de la 5e Division du Canada (5 CDSB) à Gagetown, au Nouveau-Brunswick. Le bassin versant de la rivière Nerepis, qui prend sa source à Gagetown, a été instrumenté d'août à septembre 2021 pour mesurer le débit et la turbidité dans la rivière principale et ses affluents afin de calibrer le modèle. Une fois calibré, le modèle simulera le ruissellement et les charges sédimentaires pour 1) les conditions actuelles ; et 2) pour les conditions associées aux stratégies d'atténuation qui pourraient être mises en œuvre, comme l'augmentation de la largeur des bandes riveraines aux endroits qui présentent un risque majeur. Abstract Erosion from military training areas can contribute significant amounts of sediment loadings into nearby streams. The Soil Water Assessment Tool (SWAT) was selected as a modelling tool for its ability to simulate the hydrological and water quality conditions observed in the military landscapes of 5th Canadian Division Support Base (5 CDSB) in Gagetown, New Brunswick. The Nerepis River watershed, located in and around Gagetown was monitored from august to September 2021 to measure discharge and turbidity, as both variables are needed to calibrate the model. Once calibrated, the model will simulate runoff and sediment loadings for 1) current conditions; and 2) for conditions associated with mitigation strategies that could be implemented, such as increasing buffer strip widths in key locations.
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 | 0.000 | 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 | 0.000 |
| 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".