Bibliographic record
Abstract
I share Dick Sibley and Joe Brownlie's dismay regarding the government's management of Covid-19 (VR, 18/25 April 2020, vol 186, pp 462-463). However, I also share the apparent scepticism of Richard Brown (VR, 16/23 May 2020, vol 186, pp 537-538) as to whether vets would have been any more successful in dealing with this pandemic than the medical profession. I do not believe that we would have been. I also take issue with some of Sibley and Brownlie's other points. First, they seem to disregard the successful preventive medicine initiatives that have improved human health today in terms of vaccination and disease screening, to name just two areas. Sibley and Brownlie twice state that the idea of spreading the epidemic over a longer period is merely a strategy to delay deaths. Surely, the aim of this approach, that of ‘flattening the curve’, is to prevent the NHS being overwhelmed and hence to reduce overall mortality both in general and among our heroic medical workers Sibley and Brownlie also claim that ‘no single disease has ever decimated a population’. The Great Plague of London in 1665–66 is believed to have killed 70,000 to 100,000 people, a fifth to a quarter of the city's population. It is generally accepted that the Black Death that ravaged Europe in the late 1340s killed in excess of 30 per cent of the British population and had a transformative effect on the country's history. These are just two examples of occasions when a single disease has had devastating effects on human populations, more than meeting the original definition of decimation, that is, the killing of one in 10. The deaths of possibly 90 per cent of the New World population following the arrival of European explorers in the late 15th century were not due to a single disease, but as a chronicler commented on the devastation of the Aztec population, ‘Many others died of starvation, because, as they were all taken sick at once, they could not care for each other.’1 This is perhaps pertinent to our current dilemma Perhaps ‘flattening the curve’ might have saved lives then as it could well do now.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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; both teacher heads agree on what is shown here.
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".