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Record W2091392040 · doi:10.1109/foci.2007.371502

The Immune System in Pieces: Computational Lessons from Degeneracy in the Immune System

2007· article· en· W2091392040 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicArtificial Immune Systems Applications
Canadian institutionsYork University
FundersEngineering and Physical Sciences Research Council
KeywordsDegeneracy (biology)Computer scienceImmune systemArtificial immune systemComputational modelDegenerate energy levelsUnified Modeling LanguageTheoretical computer scienceArtificial intelligenceComputational biologyBioinformaticsBiologyProgramming languageImmunologyPhysics

Abstract

fetched live from OpenAlex

The concept of degeneracy in biology, including the immune system, is well accepted and has been demonstrated to be present at many different levels. We explore this concept from a computational point of view and demonstrate how we can use computational models of degeneracy to aid the development of more biologically plausible artificial immune systems (AIS). The outcome of these models has lead us to perform an analysis of the receptor dynamics in the model and we discuss the computational implications of a "degenerate" repertoire. Through the use of the unified modelling language (UML) we have abstracted a high level immune inspire algorithm that will be used as part of a larger project to develop an immune inspired bioinformatics system

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.256
Teacher spread0.240 · 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

Quick stats

Citations24
Published2007
Admission routes1
Has abstractyes

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