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Record W2090444045 · doi:10.1002/eji.200737443

The T cell antigen receptor: “The Hunting of the Snark”

2007· review· en· W2090444045 on OpenAlexaff
Tak W. Mak

Bibliographic record

VenueEuropean Journal of Immunology · 2007
Typereview
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsT-cell receptorBiologyMajor histocompatibility complexT cellGeneGeneticsImmune system

Abstract

fetched live from OpenAlex

The quest to clone the genes encoding the T cell antigen receptor (TCR) was a tale akin to Lewis Carroll's "The Hunting of the Snark". After a long and often frustrating search, back-to-back papers reporting the discovery of the genes encoding the mouse and human TCR were finally published in the March 8, 1984 edition of Nature. In this account, I outline how my laboratory hunted the human form of the Snark, and what our discovery meant to both the immunology community and me personally. Since the isolation of the TCR genes 23 years ago, more than 30,000 papers have been published on subjects as varied as central tolerance, peripheral tolerance, TCR crystal structure, and how TCR bind to peptide/MHC structures. Numerous clinical studies involving aspects of TCR biology have also been reported. Here, I briefly discuss how the knowledge generated from our discovery has been applied to basic and clinical immunology, and where it might take us in the future.

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 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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.353
Teacher spread0.284 · 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 teacher head, not a consensus.

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

Citations20
Published2007
Admission routes1
Has abstractyes

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