An immunologist, ecologist and clinician walk into Plato’s cave to discuss infections
Bibliographic record
Abstract
Abstract If the immune system is an interconnected network, then the evolution of an archetype that is ideal for fighting one pathogen should result in tradeoffs decreasing its ability to fight others. How many archetypes are there in an immune system? We infected diverse mice with Plasmodium chabaudi, and identified five distinct archetypes of responses based on the host’s position in microbial load, immune activity, and host damage space. To better understand the nature of these archetypes, we developed a mathematical model of a generalized host-pathogen system. This model explains the number, and distribution of archetypes across a population of diverse hosts. Mice resilient to P. chabaudi exhibited poor outcomes when challenged with influenza, SARS-CoV-1, or Mycobacterium tuberculosis , and vice versa, supporting our tradeoff hypothesis.
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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.027 |
| Insufficient payload (model declined to judge) | 0.031 | 0.011 |
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".