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Record W4362552710 · doi:10.1158/2326-6066.22537889

Supplementary Table 1, Figures 1 - 3 from Optimization of T-cell Reactivity by Exploiting TCR Chain Centricity for the Purpose of Safe and Effective Antitumor TCR Gene Therapy

2023· supplementary-materials· en· W4362552710 on OpenAlexafffund
Toshiki Ochi, Munehide Nakatsugawa, Kenji Chamoto, Shinya Tanaka, Yuki Yamashita, Tingxi Guo, Hiroshi Fujiwara, Masaki Yasukawa, Marcus O. Butler, Naoto Hirano

Bibliographic record

Venuenot available
Typesupplementary-materials
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoOntario Institute for Cancer Research
FundersNational Institutes of HealthUniversiteit LeidenPrincess Margaret Cancer Foundation
KeywordsT-cell receptorJurkat cellsCD3CD8Molecular biologyTetramerChemistryT cellGeneBiologyAntigenGeneticsBiochemistryImmune systemEnzyme

Abstract

fetched live from OpenAlex

Supplementary table S1. Amino acid sequences of A24/WT1235-related peptides. Supplementary figure S1. The TAK1β hemi-chain has a dominant role in dictating A24/WT1235 reactivity. Supplementary figure S2. All forty-five Jurkat 76/CD8 TCR transfectants express similar levels of CD3/TCR complexes. Supplementary figure S3. Jurkat 76/CD8 TCR transfectants show different degrees of A24/WT1235 tetramer positivity.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.780
Threshold uncertainty score0.313

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.7800.386

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 head0.026
GPT teacher head0.299
Teacher spread0.274 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2023
Admission routes2
Has abstractyes

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