Trade-offs between acquired and innate immune defenses in humans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Immune defenses provide resistance against infectious disease that is critical to survival. But immune defenses are costly, and limited resources allocated to immunity are not available for other physiological or developmental processes. We propose a framework for explaining variation in patterns of investment in two important subsystems of anti-pathogen defense: innate (non-specific) and acquired (specific) immunity. The developmental costs of acquired immunity are high, but the costs of maintenance and activation are relatively low. Innate immunity imposes lower upfront developmental costs, but higher operating costs. Innate defenses are mobilized quickly and are effective against novel pathogens. Acquired responses are less effective against novel exposures, but more effective against secondary exposures due to immunological memory. Based on their distinct profiles of costs and effectiveness, we propose that the balance of investment in innate versus acquired immunity is variable, and that this balance is optimized in response to local ecological conditions early in development. Nutritional abundance, high pathogen exposure and low signals of extrinsic mortality risk during sensitive periods of immune development should all favor relatively higher levels of investment in acquired immunity. Undernutrition, low pathogen exposure, and high mortality risk should favor innate immune defenses. The hypothesis provides a framework for organizing prior empirical research on the impact of developmental environments on innate and acquired immunity, and suggests promising directions for future research in human ecological immunology.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it