A Comparison of Data Sources for Manual and Automated Hydrographical Network Delineation
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Bibliographic record
Abstract
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. Cette tude a t conduite pour valuer l'exactitude des rseaux hydrologiques drivs a partir de deux modles numriques d'altitude (DEM) et de deux images de tldtection pour un tributaire de la rivire Frenchman dans une rgion au sud-ouest de la Saskatchewan. Ce projet fournira galement de l'information pratique en ce qui concern l'utilisation du Indian Remote Sensing Satellite (IRS) pour la dlination hydrologique de rseau. Des images et les orthophotographs de IRS ont t employs pour la dlination de rseau manuel. Les donnes canadiennes d'altitude de Numrique (CDED) et un modle numrique d'altitude (DEM) construit a partir d'information d'altitude de point et de ligne des orthophotographs ont t employs pour la dlination automatise de rseau dans le programme TOPAZ (paramtrisation topographique). Chaque rseau trac a t compar au mme rseau topographique national ligne bleue de la srie (NTS), car le rseau de NTS tait la reprsentation disponible la plus prcise du rseau rel. Les rseaux ont t compars par le recouvrement visuel, l'index de Kappa (KIA), et les statistiques de rseau telles que des rapports de longueur et bifurcation des jets. Le IRS, l'orthophotograph, et les rseaux de CDED taient convenable comme source d'information pour la dlination de rseau, tandis que le DEM ortho- n'tait pas. Les diffrences principales entre les rseaux taient dans les premiers jets d'ordre et les effets de consquent sur des jets d'ordre plus suprieur. Il est difficile de tracer les premiers jets d'ordre d'une faon consistante et prcise dans l'environnement numrique en raison de la diffrente source d'information et de variation entre logiciels utiliss lors du processus de calcul. En consquence, on conclut que des enqutes de champ devraient tre considres en mme temps que la manipulation numrique pour la classification prcise des premiers jets d'ordre.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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