Sequential Immune Responses: The Weapons of Immunity
Bibliographic record
Abstract
Sequential immune responses (SIR) is a new model that describes what 'immunity' means in higher animals. Existing models, such as self/nonself discrimination or danger, focus on how immune responses are initiated. However, initiation is not protection. SIR describes the actual immune responses that provide protection. SIR resulted from a comprehensive analysis of the evolution of immune systems that revealed that several very different types of host innate responses occur (and at different tempos) which together provide host protection. SIR1 uses rapidly activated enzymes like the NADPH oxidases and is present in all animal cells. SIR2 is mediated by the first 'immune' cells: macrophage-like cells. SIR3 evolved in animals like invertebrates and provides enhanced protection through advanced macrophage recognition and killing of pathogens and through other innate immune cells such as neutrophils. Finally, in vertebrates, macrophages developed SIR4: the ability to present antigens to T cells. Though much slower than SIR1-3, adaptive responses provide a unique new protection for higher vertebrates. Importantly, newer SIR responses were added on top of older, evolutionarily conserved functions to provide 'layers' of host protection. SIR transcends existing models by elucidating the different weapons of immunity that provide host protection in higher animals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".