Bibliographic record
Abstract
Vaccination is an effective method to prevent individuals from contracting diseases like measles and the flu. Its success is clearly demonstrated by the large declines in the incidence of many diseases (e.g., childhood diseases like measles) after the start of mass vaccination programs. However, what happens after this drop in incidence can be complicated. It is known that some diseases exhibit "honeymoon periods" (long periods of temporary low disease incidence after start of mass vaccination). These periods end with a natural resurgence of the disease, which is not due to any change in the system. To study honeymoon periods, we used the compartmental model analyzed in [1] that can exhibit different types of vaccine failures: failure in degree (leakiness), in take (all-or-nothing) and in duration (waning of vaccine-derived immunity). We showed that traditional measures of transient dynamics in ecology may not distinguish between models with different honeymoon periods. We also provide a proof of global stability of the endemic equilibrium when the reproduction number (accounting for vaccination) is greater than one, and introduce a technical definition of the honeymoon period.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".