How to quantify developmental synchrony in malaria parasites
Bibliographic record
Abstract
Malaria infections represent an iconic example of developmental synchrony, where periodic fevers can result when the population of parasites develops synchronously within host red blood cells. The level of synchrony appears to vary across individual hosts and across parasite species and strains, variation that—once quantified—can illuminate the ecological and evolutionary drivers of synchrony. Yet current approaches for quantifying synchrony in parasites are either biased by population dynamics or unsuitable when population growth rates vary through time, features ubiquitous to parasite populations in vitro and in vivo . Here we develop an approach to estimate synchrony that accounts for population dynamics, including changing population growth rates, and validate it with simulated time series data encompassing a range of synchrony levels in two different host-parasite systems: malaria infections of mice and human malaria parasites in vitro . This new method accurately quantifies developmental synchrony from per capita growth rates using obtainable abundance data even with realistic sampling noise, without the need to sort parasites into developmental stages. Our approach enables variability in developmental schedules to be disentangled from even extreme variation in population dynamics, providing a comparative metric of developmental synchrony.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".