Bibliographic record
Abstract
Randomized controlled trials of veterinary vaccines are essential if we are to have a reasonable understanding of how those vaccines can be expected to perform when used in the field. This manuscript reviews a few (but certainly not all) of the key elements that need to be considered in the design of veterinary vaccine trials. The first step in the design of such a trial is to have a clear statement of the objective of the trial that reflects what is expected of the vaccine (e.g. should it minimize clinical disease or does it need to prevent infection?). Because domestic animals are often managed in groups, the 'unit of concern' used in a vaccine trial becomes of great importance. Whether the trial should be carried out at the individual or group level will depend on the objectives of the trial and the extent of concern about 'group effects' affecting the trial. Sample sizes will also be influenced heavily by the choice of unit of concern and the nature of the primary outcome being assessed. Finally, while there is no easy solution (except to conduct group-level trials, which may be logistically impossible), the potential for group effects to influence the trial outcome must be considered.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.690 | 0.927 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.071 | 0.009 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".