Bibliographic record
Abstract
Dr Gardiner [1] did not discuss the duty of care of those who can do most to protect us in an infective epidemic: our employers. Not to use available protection would, as he says, be a suicide mission. Our military commanders are already criticised for not providing protective equipment to soldiers in the field. It is equally wrong, both ethically and pragmatically, to deny protection those fighting a medical battle. Despite electricity grid failures being rare, hospitals in the UK have generators that are expensive to provide and to maintain, and they are regularly tested. However, I know of no hospital that has any equipment to protect staff from infection to the level recommended by the Toronto team [2], let alone practices its use regularly. Half of the 267 people admitted at Toronto with suspected Severe Acute Respiratory Syndrome were health workers, including three anaesthetists and an intensivist. Twenty-one people died. Not to protect such people endangers an essential hospital asset and the lives of its employees, and is a failure of the employers’ duty of care, ‘to ensure, as far as possible, your health, safety and welfare while you’re at work’ [3].
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.045 | 0.044 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".