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Record W4407615141 · doi:10.1111/sji.70002

On the nature of signal 1 delivered to lymphocytes: A critical response to some considerations put forward in support of the quantum model of T cell activation

2025· review· en· W4407615141 on OpenAlexafffund
Peter A. Bretscher

Bibliographic record

VenueScandinavian Journal of Immunology · 2025
Typereview
Languageen
FieldImmunology and Microbiology
TopicT-cell and B-cell Immunology
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAntigenLymphocyteLymphocyte activationImmunologyCell biologySIGNAL (programming language)BiologyVirtueNeuroscienceT cellComputer scienceImmune systemPolitical science

Abstract

fetched live from OpenAlex

The original Two Signal Model of lymphocyte activation stated that antigen-dependent lymphocyte cooperation is required for lymphocyte activation, whereas a single or a few antigen-specific lymphocytes can be inactivated by antigen. A virtue of this model is its ability to account for peripheral tolerance. Both the activation and inactivation of lymphocytes were envisaged to require the lymphocytes' antigen-specific receptors to interact with antigen, leading to signal 1. We consider here the proposition that the sensitivity to antigen concentration for the generation of signal 1, to support both differentiation processes, is the same. This situation optimizes the reliability of peripheral tolerance and minimizes the effects of lymphocyte inactivation in decreasing the diversity of the lymphocytes. We consider the broader implications of this Principle of Parsimonious Sensitivity in regulating the activity of lymphocytes.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0020.005
Open science0.0020.001
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.022
GPT teacher head0.290
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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