Book Reviews 297 Brain Trust: The Hidden Connection Between Mad Cow and Misdiagnosed Alzheimer's Disease by Colm A. Kelleher. Paraview Pocket Books, 2004. 312 pp. $22.00 US, $32.00 Canada (hardcover). ISBN 0-7434-9935-2.
Bibliographic record
Abstract
It is troubling, first, because of the subject matter. Kelleher's topic is the relatively new (at least to science) family of brain-wasting diseases known as transmissible spongiform encephalopathies (TSEs). Specific animal TSEs include the bovine affliction BSE, or "Mad Cow Disease, " which has devastated the British and European cattle industries; scrapie, a disease of sheep known for more than two centuries; Chronic Wasting Disease (CWD), which is killing North American deer and elk; and a variant seen in farmed mink (TME), which may be linked, through the mink's feed, to an endemic but undocumented North American variety of BSE. The two best-known human TSEs are kuru, which infected the Fore tribe of New Guinea and was spread by cannibalism, and Creutzfeldt-Jacob Disease (CJD). The latter has been known for decades primarily as a "sporadic " disease of undetermined origin, with less common iatrogenic and familial strains. Since 1996, though, a so-called "new variant" CJD (vCJD), with more than 150 victims thus far in the UK and Europe, has been attributed to eating meat from BSE-infected cows.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.183 | 0.115 |
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".