The Problem of Classification when the Data are Non-precise
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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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 | 0.023 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".