Declarative generation of synthetic XML data
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
Abstract Synthetic data can be extremely useful in testing and evaluating algorithms, tools and systems. Most synthetic data generators available today are the result of individual benchmarking efforts. Typically, these are complex programs in which the specifications of both the structure and the contents of the data are hard‐coded . As a result, it is often difficult to customize these tools for producing synthetic data tailored for specific needs. In this article, we describe the ToXgene synthetic data generator, which is a declarative tool for generating realistic XML data for benchmarking as well as testing purposes. We present our template specification language, which consists of augmenting XML Schema with probabilistic models that guide the data‐generation process. We discuss the architecture of our current implementation and we argue about ToXgene's usefulness by discussing experimental results as well as describing two projects that use our tool. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.004 |
| 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