HydroSHEDS-X: A new source for hydro-ecologial research and applications in the Canadian Arctic
Bibliographic record
Abstract
HydroSHEDS is a well-established database for large scale hydro-ecological research and applications worldwide. The first version was introduced in 2008 and offers global digital hydrographic information including river network and catchment boundary delineation at multiple scales. It is widely used in hydrological modelling and applications in a broad range of disciplines including environmental, conservation, socio- economic, human health, and sustainability studies. The first version of HydroSHEDS was derived from the digital elevation model (DEM) of the Shuttle Radar Topography Mission (SRTM) at a pixel resolution of 3 arc-seconds. However, SRTM data are not available for latitudes higher than 60 degrees north. Therefore, areas like the Canadian Arctic were filled with Hydro1k elevation data, which is of lower quality and only available at a coarser resolution. The new HydroSHEDS v2.0 - also known as HydroSHEDS-X - is currently derived from the TanDEM-X data set (TerraSAR-X add-on for Digital Elevation Measurement) in an international collaborative project between the German Aerospace Center (DLR), McGill University, Confluvio Consulting, and World Wildlife Fund. TanDEM-X offers a homogeneous global DEM in 0.4 arc-second resolution (~12 m grid cell size). This DEM is first processed and upscaled to derive a pre-conditioned DEM at 3 arc-second resolution. In a second step, refined hydrological optimization procedures are applied to derive the drainage pathways in the same format as the established HydroSHEDS product but at improved quality and with full coverage of the Arctic region. Other products related to HydroSHEDS, such as the hydro-environmental catchment and river reach characteristics offered in HydroATLAS, will also be updated. This poster introduces the new HydroSHEDS-X database, briefly describes its processing and quality, and finally demonstrates the improvements for Banks Island as a test site in the Canadian Arctic.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 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 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".