Stream network modelling using lidar and photogrammetric digital elevation models: a comparison and field verification
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A conventional, photogrammetrically derived digital elevation model (DEM; 10 m resolution) and a light detection and ranging (lidar)‐derived DEM (1 m resolution) were used to model the stream network of a 193 ha watershed in the Swan Hills of Alberta, Canada. Stream networks, modelled using both hydrologically corrected and uncorrected versions of the DEMs and derived from aerial photographs, were compared. The actual network, mapped in the field, was used as verification. The lidar DEM‐derived network was the most accurate representation of the field‐mapped network, being more accurate even than the photo‐derived network. This was likely due to the greater initial point density, accuracy and resolution of the lidar DEM compared with the conventional DEM. Lidar DEMs have great potential for application in land‐use planning and management and hydrologic modelling. The network derived from the hydrologically corrected conventional DEM was more accurate than that derived from the uncorrected one, but this was not the case with the lidar DEM. Copyright © 2007 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it