UEA – CRU Review Initial Report and commentary on email examination
Bibliographic record
Abstract
(CRU) at the University of East Anglia (UEA) appeared on various websites and were subjected to hostile interpretation on various “blog ” sites and in the mainstream media. They were believed to have been “hacked ” from a CRU back-up server which itself was removed by the Norfolk Police as part of their investigation into the “hack”. I am asked by the University of East Anglia to look at the back-ups of the computers of the key researchers in CRU as they are held on the back-up server to see if it is feasible to identify email traffic which was not publicised on the various websites, but nonetheless related to the same issues and might justify further investigation by the Independent Review into the publication of the emails and the allegations of inappropriate scientific and other practice which had subsequently been made. 2. I am not part of the Review Team headed by Sir Muir Russell, nor have I any part in the investigations by Norfolk Police. I have at this stage no knowledge of the technical means by which the emails were acquired from the CRU. 3. I have been supplied by the University with a “thumb drive ” said to contain copies of all the emails known to have been published on the websites. I have also been supplied by
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.
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 | 0.085 | 0.420 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.026 | 0.022 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".