Bibliographic record
Abstract
The international race to find a preventive vaccine and effective treatments against COVID 19 has been influenced by two fundamental factors. Firstly, by the molecular characterization of the causative virus and the pathology it produces, and secondly, by access to this information, mostly free of charge by the international scientific community causing a synergy to obtain results in such a short time. Several vaccines preparations against Covid19 have entered Phase 3 clinical trials. Although it is uncertain the degree of protection that they will achieve, preliminary data from Phase 1 and 2 trials and studies in animals indicate that they trigger an antiviral immune response without serious side effects. The current formulations include viral vectors, RNA vaccines, inactivated viruses, and recombinant proteins particles. They all have advantages and disadvantages, but only the results of Phase 3 clinical trials will ultimately decide the best candidates for vaccination campaigns. The tremendous impact of the SARS-CoV-2 in our society has triggered an unprecedented effort to find a vaccine to control the pandemic. Billions of dollars have already been invested in multiple vaccination schemes. According to the WHO, more than 170 vaccines were in different phases of development in August 2020. Here is a summary of the advantages and disadvantages of the front runner strategies categorized according to their delivery method.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".