Using the k-Means Clustering Algorithm to Classify Features for Choropleth Maps
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Bibliographic record
Abstract
Common methods for classifying choropleth map features typically form classes based on a single feature attribute. This technical note reviews the use of the k -means clustering algorithm to perform feature classification using multiple feature attributes. The k -means clustering algorithm is described and compared to other common classification methods, and two examples of choropleth maps prepared using k -means clustering are provided. Abstract: Les methodes courantes de classification des entites des cartes choroplethes forment habituellement des classes basees sur un seul attribut d’entite. Cette note technique passe en revue l’utilisation de l’algorithme de classification automatique a k -moyenne pour classer les entites au moyen d’attributs d’entites multiples. L’auteur decrit l’algorithme de classification automatique a k -moyenne, le compare a d’autres methodes de classification courantes et fournit deux exemples de cartes choroplethes preparees par classification automatique a k moyenne.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it