Mapping outlet points used for watershed delineation onto DEM‐derived stream networks
Bibliographic record
Abstract
Outlet point positions taken from hydrometric stations commonly do not coincide with stream locations extracted from digital elevation models (DEMs). This is a serious problem for accurate watershed delineation of data sets containing numerous outlets, which is critical in regional‐scale studies that relate catchment characteristics to basin responses. The advanced outlet repositioning approach (AORA), presented here, replicates the processes involved in manual outlet placement while reducing inefficiency and potential for blunders. The technique uses water body names to identify locations for outlet repositioning that are consistent with nearby outlets. The AORA performance was compared against two existing automated techniques using 993 stations in seven basins in northwest England. The AORA had the fewest repositioning errors in each basin and nearly halved the overall number of errors in the data set, compared with the second‐best method. This work highlights the potential errors that may be present in studies that have employed existing automated watershed mapping methods.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".