Remote sensing of river habitat for salmon restoration
Bibliographic record
Abstract
Losses of river complexity and viable habitat has led to negative effects on Atlantic salmon. With the rapid population decline of Atlantic salmon, there has been an increase in river restoration and salmon reintroduction projects, and an understanding of substrate is a vital component in the restoration of these habitats. However, the isolation and/or inaccessibility of many of these rivers make the collection of this information challenging and expensive based on conventional survey approaches. This study looks at the feasibility and accuracy of conducting substrate analysis using low-cost uncrewed aerial vehicles (UAV) at seven transects through macroscale river habitat (riffles, runs and pools) on the Upper Salmon River located in Fundy National Park near Alma, New Brunswick, Canada. Using ArcGIS, a supervised classification was conducted separating the dry and submerged substrate for higher accuracy. An object-based image analysis was conducted in PCI for delineation of substrate size. Small ideal spawning substrate was found to be concentrated in slower flowing pools while large substrate was concentrated in faster flowing riffles. The substrate analysis was conducted with an accuracy of 79% for dry substrate and 86% for submerged substrate, demonstrating the potential of UAV use in salmon habitat analysis.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".