Evaluation of River Network Generalization Methods for Preserving the Drainage Pattern
Bibliographic record
Abstract
The drainage pattern of a river network is the arrangement in which a stream erodes the channels of its network of tributaries. It can reflect the geographical characteristics of a river network to a certain extent because it depends on the topography and geology of the land and as such should be considered during the river network generalization process. There are many methods for river network generalization in tributary selection but most do not explicitly consider the network pattern. Validation of the generalized result is performed visually by an expert and may not be done systematically. An automatic validation technique may help to better understand the results obtained with each method and check whether the pattern has been preserved. This paper proposes an approach to evaluate the quality of a generalized river network by assessing how well its original drainage pattern is preserved. The membership to a drainage pattern is evaluated by a set of geometric indicators, making use of a fuzzy logic approach which allows for a compromise between different criteria depending on the membership values. Three tributary selection methods are tested in this work: selection by stroke and length, catchment area, and a manually generalized network. Assessing the quality of a generalization is done by comparing pattern memberships before and after generalization. This research provides a quantitative indicator to assess the generalized river network in preserving geographical information.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".