spbal: An R package for spatially balanced master sampling
Why this work is in the frame
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Bibliographic record
Abstract
Summary One of the most critical design features for sampling spatial populations is being able to draw spatially balanced samples. A substantial body of literature on sampling methodology has shown that spatially balanced samples can improve the precision of commonly used design‐based estimators in various settings. Spatially balanced master samples offer several practical advantages for practitioners, including adjusting the sample size to match budgetary constraints, intensifying a previous sample or defining a panel design for surveying over time. These designs are of practical importance and should be easy to generate with reliable and efficient software. The spbal R package provides explicit functionality for spatially balanced master sampling designs from point and areal resources. Stratified and panel designs are also possible with spbal . In this article, we demonstrate the flexibility of spbal with several example designs using spatial populations from New Zealand.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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