B cell/antibody tolerance to our own antigens
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
The lymphoid system normally mounts damaging responses to infectious pathogens while avoiding equally damaging responses to self. A notable number of antibodies to self antigens are formed but normally remain at levels below the damaging threshold, only temporarily rising to damaging levels during protective responses against infectious nonself. Many mechanisms regulate the level of autoantibodies and anti-self B cells including deletion, anergy, ignorance for antigen, receptor editing, coinhibition, competition for resources to sustain B cell responses, and apoptotic denouement of damaging responses following the ejection or containment of foreign invaders. While infectious events may encourage immune responses to self antigens, infectious events tend also to strengthen regulatory mechanisms. When regulatory mechanisms do not function properly, abnormal damaging responses to self antigens may occur. While defects in a single regulatory mechanism may result in autoimmunity, this eventuality usually happens only on permissive genetic backgrounds; this indicates that weakness in other regulatory mechanisms may be necessary to result in the emergence of damaging responses to self antigens. The immune system and its regulatory mechanisms are not simple, as one would expect of a homoeostatic process that also has the ability to expand enormously when challenged and to contract rapidly when threats pass. These processes that avoid damaging anti-self B cells are much more complicated than that envisaged in standard two signal models. Simple signals through the B cell antigen-receptor probably encourage B cell survival and receptivity, while other signals (costimulatory or coinhibitory) promote B cell stimulation or non-stimulation/inactivation.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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