Intelligent Use of Mask in the View of Extraordinary Shortage Situation of COVID-19 Pandemic
Bibliographic record
Abstract
In the current situation of Coronavirus Disease (COVID-19) pandemic, frontline workers are trying hard with maximum possible efforts to lessen the transmission of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) with the help of available resources. Due to existing shortages of the Personal Protective Equipments (PPE) more frequently facemasks and respirators, the rational use of these PPEs and their prioritisation becomes absolutely necessary. Other measures such as the use of respirators beyond their shelf-life, extended usage, limited reuse of respirators and decontamination, reprocessing and re-use of respirators have to be considered. This review article focused on the current situation of shortage of masks, rational use of various types of respiratory protective devices, mask use in laboratories and different methods of decontamination and reprocessing of the respirators. Intense literature search of ongoing COVID-19 pandemic and Influenza pandemic 2009 were done and various guidelines inclusive of Centre for Disease Control and prevention (CDC), World Health Organisation (WHO), Public health agency of Canada were adapted for this review.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".