Bibliographic record
Abstract
Human papillomavirus virus is one of the common infectious diseases in the world. HPV causes around 90% of the cervical cancers [1] and other diseases in males, females and bisexuals. In this article, the HPV vaccine is discussed as an effective way to prevent HPV intervention. The currently available HPV vaccines are 2, 4 and 9 valent which are all included in this article. The 2-valent targets 16, 18 types of HPV, the 4-valent and 9- valent can target 6, 11, 16, 18; 6, 11, 16, 18, 31, 33, 45, 52, and 58 types of HPV. The target populations of these 3 HPV vaccines are similar. Need to mention that many gender populations (males, females and bisexuals included) are all encouraged to take HPV vaccine at certain ages. The limitations of HPV vaccines cause the inhibition of the prevention of HPV and low inoculation rates worldwide, especially in developing countries. Limitations include inoculation age, target HPV types and vaccine price. This article also proposes a future tendency of research may on resolving these restrictions and promoting HPV vaccines in teenagers.
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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".