The Problem of Classification when the Data are Non-precise
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
Non-precise data arise in a natural way in several contexts. For example, the water level of a river does not usually consist of a single number as can be seen from the intensity of the wetness as a function of depth of a survey rod. The temperature of a room varies as a function of distance from a reference point. The color intensities associated with a pixel which describe observations from remote sensing are non-precise numbers because they vary as a function of the reflection from the sun. In these examples, it is the imprecision of the observation itself that is of interest rather than the uncertainty due to statistical variation. Even in the absence of stochastic error, there would still be an imprecision in the measurement. Viertl (1997) developed the subject of statistical inference for such non-precise data and associated it very closely to fuzzy set theory. Precise data can be described by an indicator function whereas non-precise data is described by characterizing functions. In this article, we first review the notation and then consider the problems of classification for non-precise data.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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