Corrigendum to “Zika Virus Causes Persistent Infection in Porcine Conceptuses and May Impair Health in Offspring”
Bibliographic record
Abstract
An earlier online version of the paper contained an error in the bioinformatics data tables. This error has now been corrected by the authors, and the changes in the tables and corresponding text have been verified with a reviewer. The online version of the paper has now been updated to include this correction. We apologize for the confusion. Zika Virus Causes Persistent Infection in Porcine Conceptuses and may Impair Health in OffspringOutcomes of Zika virus (ZIKV) infection in pregnant women vary from the birth of asymptomatic offspring to abnormal development and severe brain lesions in fetuses and infants. There are concerns that offspring affected in utero and born without apparent symptoms may develop mental illnesses. Therefore, animal models are important to test interventions against in utero infection and health sequelae in symptomatic and likely more widespread asymptomatic offspring. To partially reproduce in utero infection in humans, we directly inoculated selected porcine conceptuses with ZIKV. Full-Text PDF Open Access
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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.130 | 0.073 |
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".