ON CLASSIFICATION OF A BIVARIATE BINARY OBSERVATION
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
Classification of a bivariate binary observation into one of the two possible groups requires the estimation of the joint cell probabilities under each of the two groups. Two widely used approaches for the estimation of such joint cell probabilities are: [1] kernel based non-parametric approach; and [2] multinomial distribution based cell counts approach. In these traditional approaches, the joint cell probabilities are estimated without making any assumptions about the structural forms for these probabilities. Consequently, it is not clear, how these traditional approaches take into account the correlation that may exist between the 2-dimensional binary observations. In this paper, we model the cell probabilities by a suitable bivariate binary distribution which accommodates the correlation in a natural way, and examine the effect of this type of modelling in classifying a new correlated binary observation into one of the two groups. This is done by comparing the probability of misclassification yielded ...
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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 | 0.006 | 0.009 |
| 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