Keeping up with YOUR Scientific Journals and YOUR Challenges
Bibliographic record
Abstract
In the summer edition of the Canadian Journal of Infection Control, Victoria Williams and Devon Metcalf referenced the Wellcome Trust and the impact of publications during the COVID-19 pandemic. As a segue into their article, and with the arrival of a new infection such as COVID-19, it has left many of us Infection Prevention and Control (IPAC) professionals trying to keep up with the latest evidence as we struggle to be grounded in the science at the same time due to the volumes of research to review. In a statement by the Wellcome Trust in January 2020, they expressed the necessity for making any information available that might have value in combatting a crisis, including research findings and data relevant to the COVID-19 pandemic. In the context of a public emergency of international concern, this data is important as it informs the public health response and helps to save lives. We need to see the evidence and findings quickly in order to assist with important decisions, guidance, and recommendations.
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 | 0.174 | 0.513 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.024 | 0.030 |
| Scholarly communication | 0.084 | 0.056 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.058 | 0.059 |
| Insufficient payload (model declined to judge) | 0.048 | 0.064 |
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".