Bibliographic record
Abstract
The importance of the negative selection of self-reacting cells from immune repertoires was easily recognized since it would militate against autoimmune disease. However, while the existence of positive selection in the auditioning of newly formed immune cells is now recognized, it is taking longer to understand its role. With the removal or suppression by negative selection of a subset of immune cells that self-react, would the specificities of remaining immune cells range widely to confront the universe of 'non-self' antigens, including some borne by potential microbial pathogens? Alternatively, from among those remaining immune cells, could some be picked out (positively selected) based on 'advanced knowledge' of some character likely to be common to those pathogens? To exploit 'holes' created by negative selection, it was predicted that pathogens would attempt to mimic their hosts by progressive stepwise mutation towards host 'self'-a process entailing passage through a host anti-'near-self-reactivity' arena. Anticipating this, those hosts that over evolutionary time 'learned' to positively select from developing immune repertoires, cells with reactivities against 'near-self', would be advantaged by natural selection. The benefits of this narrowing of the range of host immune reactivities are now clearer and may solve Burnet's paradox, which concerns the ability of an organism's immune cells to attack its own cancer cells. However, while supporting this in the context of T cell immunity, Manczinger and his colleagues now suggest that some wily pathogens may be exploiting their hosts' narrow defence foci by 'seeking', through mutation, dissimilarity from host 'self'.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".