Suspended sediment modelling in the Nerepis River system. Field Sampling report
Bibliographic record
Abstract
Landscape erosion and sedimentation of watercourses has been identified as a serious environmental issue at 5th Canadian Division Support Base (5 CDSB) Gagetown. To address this issue, the Department of National Defence has been conducting remediation works through a Sedimentation and Erosion Control Program (SECP). Work has included upgrades and decommissioning or roads, trails and water-crossings, re-vegetation of barren soils and stream restoration. Despite these efforts, landscape erosion and watercourse sedimentation continue to be an issue. A hydrological and suspended sediment models for the Nerepis River and Kerr Brook were developed under the Sedimentation and Erosion Control Program SECP, using the ArcGIS Soil Water Assessment Tool (SWAT). Some of the data that are needed to calibrate and validate the model were collected between 2009 and 2013. As the model will be completely re-calibrated as part of the present project, field monitoring was re-initiated during the summer of 2021. This report provides a summary of this field investigation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".