A short overview of the CRISPR-Cas adaptation stage
Bibliographic record
Abstract
CRISPR research began over 30 years ago with the incidental discovery of an unusual nucleotide arrangement in the Escherichia coli genome. It took 20 years to find the main function of CRISPR-Cas systems as an adaptive defence mechanism against invading nucleic acids, and our knowledge of their biology has steadily increased ever since. In parallel, the number of applications derived from CRISPR-Cas systems has risen spectacularly. The CRISPR-based genome editing tool is arguably the most exciting application in both basic and applied research. Lately, CRISPR-Cas research has partially shifted to the least understood aspect of its biology: the ability of CRISPR-Cas systems to acquire new immunities during the so-called adaptation step. To date, the most efficient natural system to readily acquire new spacers is the type II-A system of the gram-positive dairy bacterium Streptococcus thermophilus. The discovery of additional systems able to acquire new spacers will hopefully draw more attention to this step of CRISPR-Cas biology. This review focuses on the breakthroughs that have helped to unravel the adaptation phase and on questions that remain to be answered.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".