Exploring associations between viral titer measurements and disease outcomes in ferrets inoculated with 125 contemporary influenza A viruses
Bibliographic record
Abstract
Ferrets are frequently employed to study the pandemic potential of novel and emerging influenza A viruses. However, systematic retrospective analyses of data generated from these experiments are rarely performed, limiting our ability to identify trends in this data and explore how analyses can be refined. Using logarithmic viral titer and clinical data aggregated from one research group over 20 years, we assessed which meaningful and statistically supported associations were present among numerous quantitative measurements obtained from influenza A virus (IAV)-infected ferrets, including those capturing viral titers, infection progression, and disease severity. We identified numerous linear correlations between parameters assessing virus replication at discrete sites in vivo , including parameters capturing infection progression not frequently employed in the field, and sought to investigate the interconnected nature of these associations. This work supports continued refinement of data analyzed from in vivo experimentation, notably from studies which evaluate the public health risk posed by IAV.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".