Reply to Skowronski et al.
Bibliographic record
Abstract
To the Editor —In the letter by Skowronski et al. [1], written in response to our study, concerns are raised about the fact that all outbreaks in poultry that bring humans into contact with avian influenza virus subtypes not circulating in humans should be cautiously managed. We fully agree with this assertion, because this is also the primary message of our work We also agree that, in humans, serological determination after outbreaks of avian influenza may be very difficult. In this regard, our article had emphasized that serum samples were considered to be positive for antibodies to the H7 subtype of avian influenza virus only if, on the basis of at least 2 different serological techniques including the microneutralization assay, they had repeatedly given unequivocally positive results We used multiple serological tests to exclude the possibility of nonspecific cross-reactions with antibodies to human influenza viruses. Although definitive evidence for active infection would include detection of either virus or viral RNA at the time of exposure or illness, the recent increasing evidence that the number of cases of transmission of avian influenza virus to humans is higher than what had previously been observed makes serological data very important and useful; when properly evaluated, these data may provide retrospective information on the circulation of avian influenza viruses in the human population, as has been demonstrated in previous reports [2, 3]
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.030 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".