Cell site analysis; testing understanding via internal consistency checks
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
This paper is aimed at Cell Site Analysis Expert Witnesses. Ground Truth Data (GTD) are essential to validation exercises, but in the UK access to practitioner-generated Call Data Records (the traces considered by Cell Site Analysis experts) are restricted, reducing opportunities for practitioners to test their understanding against real-world data. This paper outlines methods by which casework material might be used to potentially detect issues within understanding of uncertainties (and therefore improve the reliability of analyses) by reviewing the properties of casework material in parallel with the casework assessment being conducted. Four case examples are given in which assessments of the reliability of understanding of uncertainties are tested (two examples for assessing Call Data Record GPRS time uncertainties, one for reliability of survey results and one for assessing the reliability of "geo" data from Encrochat examinations). The methods proposed are intended to provide a deeper layer of Quality Assurance; they are not intended to replace validation using GTD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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