Mosquito Vectors of Infectious Diseases : Are They Neglected Health Disaster in Egypt ?
Bibliographic record
Abstract
In spite of the great technological progress achieved worldwide, still arthropod borne infectious diseases is a puzzle disturbing the health authorities. Among these arthropods, mosquitoes from medical, veterinary and economic point of view top all groups. They are estimated to transmit disease to more than 700 million people annually worldwide mainly in Africa, South America, Central America, Mexico and much of Asia with millions of deaths. In Europe, Russia, Greenland, Canada, the United States, Australia, New Zealand, Japan and other temperate and developed countries, mosquito bites are now mostly an irritating nuisance; but still cause some deaths each year. Mosquito-borne diseases include Malaria, West Nile Virus, Elephantiasis, Rift Valley Fever, Dengue Fever, Yellow Fever and Dog Heartworm....etc. Apart from diseases transmission, mosquitoes can make human life miserable. The successful long term mosquito control requires the ecological and biological knowledge of where and how they develop. The importance of mosquitoes is given herein to clarify the problem and to think together what one must do?
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".