The right allocation of human resources for effective protection against Covid-19
Bibliographic record
Abstract
«The novel Corona Virus 2019» or «COV-2019», «CO» stands for corona, «VI» for virus, and «D» for disease. As its name indicates, the COVID-19 virus is a new strain of the same family as other viruses such as Severe Acute Respiratory Syndrome (SARS) and some common types of colds. According to the World Health Organization (WHO), the disease has been rapidly spreading since its first appearance in Wuhan, China, in December 2019. Over a quarter of a million deaths were registered worldwide. After the confirmation of the first case of coronavirus in Morocco on March 4, 2020, the shortage of protective equipment, has led to a growing demand especially for protective masks, and particularly since the government has suggested to the public to wear a mask outdoor. The sector that has responded immediately to the emergency call of the Moroccan strategy to produce and deliver the maximum number of protective masks is the textile industry, as it was noted by the Amith. This commitment was reflected in a temporary change of activities in several companie. In this sense is part of our article which deals with the problem of scheduling human resources, the objective of which is to set up a tool to help allocate these resources to the different tasks and to the different operations.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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; 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".