A Comparison of Data Sources for Manual and Automated Hydrographical Network Delineation
Bibliographic record
Abstract
This study was conducted to evaluate the accuracy of hydrological stream networks derived from two digital elevation models (DEM) and two remote sensing images for a tributary of the Frenchman River in southwest Saskatchewan. This project also provides practical insight into the use of Indian Remote Sensing (IRS) Satellite imaging for hydrological network delineation. IRS images and orthophotographs were used for manual network delineation. Canadian Digital Elevation Data (CDED) and a digital elevation model (DEM) constructed from point and line elevation information from the orthophotographs were used for automated network delineation in program TOPAZ (TOpographic PArameteriZation). Each delineated network was compared with the same National Topographic Series (NTS) blue-line network, as it was assumed the NTS network was the most accurate available representation of the actual drainage network. The networks were compared by visual overlay, Kappa Index of Agreement (KIA) and network statistics such as bifurcation and stream length ratios. The IRS, orthophotograph, and CDED networks were suitable data sources for network delineation, whereas the ortho DEM was not. The major differences between the networks were in the first order streams with consequent effects on higher order streams. First order streams are difficult to delineate in a consistent and accurate manner in a digital environment because of the nature of data sources and differences in the computation processes. As a result, it is concluded that field surveys should be considered in conjunction with digital manipulation for the accurate classification of first order streams.
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".