Bibliographic record
Abstract
A team of researchers led by Alan Davidson at the University of Toronto have discovered a small protein that can disassemble a stable bacterial CRISPR-Cas7 complex without using an apparent energy source ( Nature 2024, DOI: 10.1038 /s41586-024-07642-3 ). The protein, dubbed AcrIF25, comes from a bacteriophage, and the CRISPR-Cas7 complex it attacks comes from the bacterium Pseudomonas aeruginosa . Joseph Bondy-Denomy of the University of California, San Francisco, says that just as bacteria evolved CRISPR as a way to combat bacteriophages, phages developed ways to thwart CRISPR. This race for survival plays out very quickly because bacteria and phages change faster than most eukaryotes do. “If you wanted to figure out new ways that have evolved in nature for one thing to inhibit another thing, looking at the CRISPR and anti-CRISPR arms race is a great place to look for that sort of mechanistic novelty, and that’s exactly what
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".