Monitoring river channel change using terrestial oblique digital imagery and automated digital photogrammetry
Bibliographic record
Abstract
Imagery acquired using a high-resolution digital camera and ground survey has been used to\nmonitor changes in bed topography and plan form, and to obtain synoptic water surface and flow\ndepth information in the braided, gravel bed Sunwapta River in the Canadian Rockies. Digital\nimages were obtained during daily low flows during the summer melt-water season to maximize\nthe exposed bed area and to map the water surface on the days with the highest flows. Images\nwere acquired from a cliff top 125m above and at a distance of 235m from the riverbed and used\nto generate high resolution orthophotos and digital elevation models (DEMs) at a ground\nresolution of 0.2m, within an area 80 x 125m. The creation of digital elevation models (DEMs)\nfrom oblique and non-metric imagery using automated digital photogrammetry can be difficult,\nbut a solution based on rotation of coordinates is described here. Independent field verification\ndemonstrated that root mean square accuracies of 0.045m in elevation were achieved.\nThe ground survey data representing river bed topography were merged with photogrammetric\nDEMs of the exposed bars. The high-flow water surface could not be surveyed directly because\nwading was dangerous but was derived by ground survey of selected accessible points and\nphotogrammetry. The DEMs and depth map provide high-resolution, continuous data on the\nchannel morphology and will be the basis for subsequent 2D flow modeling of velocity and shear\nstress fields. The experience of using digital photogrammetry for monitoring river channel change\nallows the authors to identify other potential benefits of using this technique for fluvial research\nand beyond.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".