The historical postulate is not the basis of self‐nonself discrimination: A response to Bretscher's proposal
Bibliographic record
Abstract
Peter Bretscher was the first to envision that the problem of self-nonself discrimination in the adaptive immune system could be solved by positing that antigen inactivates single lymphocytes, whereas antigen-mediated lymphocyte cooperation is required to stimulate their activation. These ideas led to a two-signal model for lymphocyte activation: an antigen-specific signal that when generated alone results in tolerance and the combination of two (or more) antigen-specific signals resulting in lymphocyte activation and immunity. This 'quorum model' is consistent with the concept known as the historical postulate that posits that the early life timing of antigen exposure is the key to self-tolerance. Bretscher proposes that the historical postulate is 'the basis, at level of the system, for self-nonself discrimination' and contends that the Danger model violates this postulate. Herein I argue that the data do not support putting the historical postulate alone at the top of the hierarchy of concepts underlying self-nonself discrimination. The location of antigen is at least as important because it determines whether central tolerance will be engaged. Location of antigen together with timing of antigen exposure are major factors determining whether quorum, the basis for self-nonself discrimination, is achieved.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.004 | 0.017 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".