Bibliographic record
Abstract
SARSThe medical, social and economic impacts have been felt around the globe.As this issue of Hospital Quarterly is prepared, the World Health Organization has declared that SARS (Sever Acute Respiratory Syndrome) cases have peaked, but warns countries not to let down their guard.At the same time, The Economist warns that this may be China's Chernobyl.At health facilities across Canada, special and taxing procedures are evident.The media has inundated the public with daily and hourly information.Policy-makers are struggling to provide leadership.Individuals can refer to their favourite media outlet for instruction on good hygiene, hospitals can read about their peer institutions on a daily basis and governments across the country read and hear encouragement from Mayor Giuliani but they abhor judgments from the World Health Organization.The social and economic costs of SARS are real: masks, shields, gowns and gloves are in short supply; doctors are unable to pay their mortgages; nurses are exhausted from their new, complex work environment; grocers have fewer customers; hotels are running at 15% capacity; taxis have fewer fares.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.303 | 0.150 |
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".