Detection of local and global outliers in mapping studies
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Bibliographic record
Abstract
Abstract In mapping studies, extreme risk areas may arise in proximity to one another in a smooth spatial surface. They may also arise as isolated ‘hotspots’ or ‘lowspots’, which are quite distinct from those of neighbouring sites. In this paper, we develop spatial methods which encompass both types of extreme risks. The former is modelled by a spatially smooth surface using a conditional autoregressive model; the latter is addressed with the addition of a discrete clustering component, which offers the flexibility of accommodating extreme isolated risks and is not limited by spatial smoothness. The autoregressive component incorporates the spatially correlated risk as a baseline surface, acknowledging that environmental activity, often spatially correlated, influences risk responses. The discrete component identifies hotspots/lowspots of activity beyond the spatially correlated baseline risk surface. Both types of extreme risk are important, but isolated extremes may provide insight into areas with potential of being a centre for future spatially correlated extreme risks. Hence these may be particularly important in terms of surveillance. A Bayesian approach to inference is employed and graphical techniques for isolating extremes are illustrated. Model assessment is conducted via cross‐validation posterior predictive checks. Three examples demonstrate the utility of the methods and case studies show the procedures to be useful for pinpointing extreme risks. In addition, sensitivity to priors is investigated. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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