Bibliographic record
Abstract
bovine TB SIR, – Instead of fiddling around trying to find ever more reasons why they should not address the problem of endemic tuberculosis (TB) in badgers (VR, December 17, 2005, vol 157, p 786) DEFRA and the Independent Scientific Group on Cattle TB should ponder the fact that, from data released by DEFRA for 1996/98, at least a quarter of the badgers in England and Wales are infected with the disease (DEFRA 2005) and it won’t go away on its own. When, in 1998, DEFRA (then MAFF) stopped publishing data from carcases of badgers examined by MAFF, infection rates in the period 1996/98 in Great Britain overall were 26 per cent and rising, with pockets of infection in Gwent and East Sussex, of 82 per cent and 44 per cent respectively (DEFRA 2005). With the ever-increasing and uncontrolled badger pop-ulation the situation cannot be expected to have improved since then. We are, however, somewhat heartened to read more recently (VR, December 24/31, 2005, vol 157, p 823) that DEFRA is at last considering ‘general culling over large, loosely specified areas... through either farmer/landowner coordinated groups or a combination of state and farmer/landowner involvement’. This approach would appear to closely resem-ble the strategy of wildlife management that we advocated, over five years ago (VR,
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.258 | 0.171 |
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".