Assessment of methods for extracting low-resolution river networks from high-resolution digital data
Bibliographic record
Abstract
A range of methods for deriving lower-resolution river networks from higher-resolution Digital Terrain Model (DTM) data are assessed at various spatial scales. Derived flow networks are compared to fine-scale hydrologically corrected rivers using a range of performance criteria. These include a measure of spatial distance between fine-scale and derived rivers, and criteria which assess errors in derived catchment area. The Network Tracing Method (NTM), a vector-based network scheme, and the COTAT+ method, a raster-based scheme, are shown to produce river networks that most closely resemble the base fine-scale river networks. COTAT+ is better at preserving catchment areas while the NTM method is often spatially closer to the base river network, especially when applied at lower resolutions, due to a higher percentage of diagonal flow paths. Automatically derived river flow networks will only ever be as good as the base DTM or flow direction data set from which they are derived.
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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 | 0.033 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".