Robustness in spatial studies I: minimax prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We develop and test robust methods for estimation and for prediction in spatial studies. We assume that a stochastic process is measured, with error, at various locations. The variance/covariance structures of this process and of the measurement errors are only approximately known; in the face of these uncertainties one is to do robust estimation and prediction. We obtain a minimax linear predictor, in which mean squared error loss is first maximized over neighbourhoods quantifying the various sources of model uncertainty, and then minimized over the coefficients of the predictor subject to a constraint of unbiasedness. Robustifications of these methods are then introduced. These are based on generalized M-estimators, and are robust against contaminated error distributions. In a simulation study the procedures afford a substantial level of robustness when the model inadequacies are present, while being almost as efficient as more classical methods otherwise. Copyright © 2004 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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