Developing Choropleth Maps of Parameter Results for Quantile Regression
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Bibliographic record
Abstract
Local regression methods can provide specific information about individual observations (places) in spatial analysis that is often useful in understanding non-stationary covariate relationships. Geographically weighted regression (GWR) is one widely used local regression model whose parameter estimates are mapped as continuous or discrete surfaces. A less frequently used local regression method used in spatial analysis is quantile regression (QR). One drawback to the use of QR is its restricted visualization possibilities; currently, regression parameter summaries for QR are visualized only as line graphs that plot parameter estimates against quantile levels. This article presents a method that also permits the spatial display of these values in a choropleth map so that QR can be added to the cartographic repertoire of exploratory data models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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