The COVID-19 pandemic – lessons (un)learned – the aftermath
Bibliographic record
Abstract
StreSzczeNIeW niniejszym artykule podsumowano wpływ pandemii COVID-19 na zdrowie publiczne.Przedstawiono główne problemy epidemiologiczne, medyczne, a także te związane z diagnostyką wirusologiczną i leczeniem COVID-19.Wskazano, jak pandemia wpłynęła na ludzkie zachowania prozdrowotne i ich przyszłe konsekwencje.Przy okazji rozwiano kilka mitów i skorygowano błędne, ale dość powszechne, opinie.Słowa kluczowe COVID-19, SARS-CoV-2, zdrowie publiczne. abStractIn this paper we are discussing and summarizing major aspects of COVID-19 pandemics and its influences on the public health.We are addressing the major issues in epidemiology, medicine, and molecular biology arose during COVID-19 pandemics.We also trying to summarize its impact on human behavior and predict future consequences.We also trying to solve several mysteries and correct few erroneous opinions.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.044 | 0.019 |
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".