Retrieving channel geometry and flow properties of the Nicolet River from satellite multispectral imagery
Bibliographic record
Abstract
The Nicolet River in Quebec is a shallow river, in danger of losing biological diversity. This study has been motivated by the need for cost-effective, efficient methods for generating geometric details and flow properties of the river in association with channel restoration. The rapid advances in satellite remote sensing offer high-resolution images of river sites. The purpose of this study is two folds: retrieve underwater depth from satellite multispectral imagery; and estimate flow properties by combing the remote sensing data with HEC-RAS 2D computations. The scope of work includes developing analytical methods and applying the methods to a 10-km section of the Nicolet River. The multispectral images used in this study are WorldView-3 images of 1.2 m pixel resolution. The computational results include the depth, velocity, and bed roughness indicator. This study has demonstrated the usefulness of satellite remote sensing techniques, combined with hydraulic modeling to quantify river flow.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".