A Genetic Algorithm for Tributary Selection with Consideration of Multiple Factors
Bibliographic record
Abstract
Abstract Drainage systems are important components in cartography and Geographic Information Systems (GIS), and achieve different drainage patterns based on the form and texture of their network of stream channels and tributaries due to local topography and subsurface geology. The drainage pattern can reflect the geographical characteristics of a river network to a certain extent. To preserve the drainage pattern during the generalization process, this article proposes a solution to deal with many factors, such as the tributary length and the order in river tributary selection. This leads to a multi‐objective optimization problem solved with a Genetic Algorithm. In the multi‐objective model, different weights are used to aggregate all objective functions into a fitness function. The method is applied on a case study to evaluate the importance of each factor for different types of drainage and results are compared with a manually generalized network. The result can be controlled by assigning different weights to the factors. From this work, different weight settings according to drainage patterns are proposed for the river network generalization.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".