Bibliographic record
Abstract
The Open Geospatial Consortium has defined "OGC WaterML 2: Part 3 - Surface Hydrology Features (HY_Features) - Conceptual Model", but not any particular implementation of it. The Common Hydrology Features (CHyF) model extends HY_Features and makes some minor changes to it required for implementation and the delivery of high performance services. HY_Features discusses catchment coverage and topological relations. In CHyF these are key ideas, as is the notion that hydrologically defined network components form elements of a mathematical graph, allowing for very fast network traversal. HY_Features defines catchments and catchment networks, as well as rivers, channels, flowpaths and hydrographic networks. The CHyF logical model specifies a profile and some extensions to HY_Features, as required to implement topological and graph relations. This starts with the definition of elementary catchments and elementary flowpaths, which are treated as fundamental elements. They are tightly specified terms corresponding to basic catchments and flowpaths in HY_Features and the basic components in the standard reach-catchment model (Maidment and Clark, 2016). If they are subdivided, the result is simply more elementary catchments and elementary flowpaths. Consequently, they are the building blocks used to form complementary coverages as well as a graph structure referred to as a hygraph. Building the hygraph necessitates that connections between features be manifest through their geometry. Divergences and distributaries are supported in CHyF, as the hygraph need not be hierarchical. Nevertheless, CHyF does recognize hierarchical drainage basins and the value in identifying them explicitly (Blodgett, et al, 2021). Different kinds of elementary catchments and elementary flowpaths are defined in CHyF. Of note is that polygonal waterbody features, or portions of such features, are treated as elementary catchments in their own right. In addition to these water catchments, several kinds of land-based elementary catchments are recognized. These model constructs are compatible with the higher level conceptual model in HY_Features, although they differ in detail from other popular implementation models. With the approach taken it becomes practical to handle very large lakes and rivers, as well as coastal ocean zones. CHyF also includes wetlands, glaciers and snowfields as kinds of hydro features; these features help complete the concept of a catchment coverage as put forward by HY_Features.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".