Cell site analysis; testing understanding via internal consistency checks
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.
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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 | 0.509 | 0.801 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".