Prediction of 2 × 2 tables of change from repeat cluster sampling of marginal counts
Bibliographic record
Abstract
Repeat cluster sampling of a binary (0,1) attribute at time 1 (Y1) and time 2 (Y2) in a finite population of discrete units is considered. All clusters contain m units and a cluster provides the marginal count of ones and zeroes at the two time points only. From these counts, we seek to predict a 2 × 2 table of the rates of no change (π11 = E[Y1Y2], π00 = E[(1 Y1)(1 Y2)]) and change (π10 = E[Y1(1 Y2)], 01 = E[(1 Y1)Y2]). Two predictors are proposed; one is derived from the temporal correlation of marginal counts and the second from the odds ratio of no change that maximizes a (pseudo-) likelihood of a non-central, hypergeometric distribution. The bias of the first is positive when there is a positive intracluster correlation of Y1, Y2, and Y1Y2, while the bias of the second is negative when the odds ratio of no change is >1. A proposed combined estimator worked well in three examples of change analysis with paired, classified Landsat images of forest cover type and cluster sampling with 3 × 3 arrays of 30 m × 30 m units (pixels). 2 × 2 tables obtained from marginal counts were superior, in terms of mean absolute error, to estimates based on a direct unit-by-unit count when the time 2 image had a root mean square registration error of 0.5 pixel relative to the time 1 image. The proposed method is intended for settings where a direct unit-by-unit estimation of the 2 × 2 table is either compromised or when data (by design) consist of marginal counts from a repeat cluster sampling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".