Time, Space and Contextual Models of the Immunity Tolerance Decision: Bridging the Geographical Divide of Zinkernagel and Hengartner's ‘Credo 2004’
Bibliographic record
Abstract
In Credo 2004, Zinkernagel and Hengartner (Z&H) have continued their challenge to the immunological community to reconsider assumptions regarding the most fundamental aspects of adaptive immunity. They have appropriately championed the role of persistent, widely distributed antigen in tolerance induction, parameters that do not figure prominently in most other models. The global theory of immunity they have developed is predominantly based on observations from studies with viruses and tumours. I suggest here that a more successful approach to generating a theory of the default rules of immunity can be obtained through the study of immunity versus tolerance in the setting of transplantation. Transplantation studies lack the confounding variable of competing evolution present in responses to specific infectious agents and tumours and, therefore, more clearly elucidate default rules of immunity. The geographical model in Credo 2004, primarily a one-signal model regulated by antigen, is contrasted with (1) Cohn's time-based two-signal model and (2) a development-context model that postulates distinct central and peripheral tolerance mechanisms.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".