Catchment-scale, high-resolution, hydraulic models and habitat maps – a salmonid's perspective
Bibliographic record
Abstract
The advent of remotely-sensed high-resolution imagery has led to the development of methods to map river bathymetry. In this study, we utilized high-resolution imagery to map river depth and quantify hydraulic habitats at the catchment scale (>1000 km2) during low flows. Using 0.3-m airborne multi-spectral imagery (resampled to 0.5 m), we mapped contiguous river depth (124 km) within a well-established Atlantic Salmon (Salmo salar) and Brook Trout (Salvelinus fontinalis) river – The Little Southwest Miramichi, New Brunswick. We built image-derived depth maps with and without field data calibration. The model without field calibration data (flow resistance equation‐based imaging of river depths) accurately described river depths (R2 = 72.7; RMSE = 0.167 m; n = 762); however, it overestimated shallow depths. The field-calibrated model removed shallow depth errors (R2 = 76.4; RMSE = 0.155 m; n = 762). We mapped velocity using a relationship between river geometry and discharge, and coalesced the field-calibrated depth and velocity maps to create Froude and Reynolds number maps. Finally, we performed an unsupervised classification model to delineate the hydraulically relevant habitat units for salmonids. This approach provides an unprecedented view of catchment-scale hydraulic habitats that will advance both hydrological process research and river resources management.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".