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Phage-Encoded Anti-CRISPR Defenses

2018· review· en· W2890107400 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

VenueAnnual Review of Genetics · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCRISPRBiologyArms raceTrans-activating crRNABacteriaComputational biologyCRISPR interferenceGeneticsImmune systemGeneCas9

Abstract

fetched live from OpenAlex

The battle for survival between bacteria and bacteriophages (phages) is an arms race where bacteria develop defenses to protect themselves from phages and phages evolve counterstrategies to bypass these defenses. CRISPR-Cas adaptive immune systems represent a widespread mechanism by which bacteria protect themselves from phage infection. In response to CRISPR-Cas, phages have evolved protein inhibitors known as anti-CRISPRs. Here, we describe the discovery and mechanisms of action of anti-CRISPR proteins. We discuss the potential impact of anti-CRISPRs on bacterial evolution, speculate on their evolutionary origins, and contemplate the possible next steps in the CRISPR-Cas evolutionary arms race. We also touch on the impact of anti-CRISPRs on the development of CRISPR-Cas-based biotechnological tools.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.905
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.390
Teacher spread0.370 · 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