Bibliographic record
Abstract
The sudden emergence and worldwide adoption of CRISPR gene-editing technology confronts humanity with unprecedented opportunities and choices. CRISPR's transformative impact on our future understanding of biology, along with its potential to unleash control over the most fundamental of biological processes, is predictable by already achieved applications. Although its origin, composition, and function were revealed only recently, close to 3000 CRISPR-based publications have appeared including insightful and diversely focused reviews referenced here. Adding further to scientific and public awareness, a recent symposium addressed the ethical implications of interfacing CRISPR technology and human biology. However, the magnitude of CRISPR's rapidly emerging power mandates its broadest assessment. Only with the participation of a diverse and informed community can the most effective and humanity-positive CRISPR applications be defined. This brief review is aimed at those with little previous exposure to the CRISPR revolution. The molecules that constitute CRISPR's core components and their functional organization are described along with how the mechanism has been harnessed to edit genome structure and modulate gene function. Additionally, a glimpse into CRISPR's potential to unleash genetic changes with far-reaching consequences is presented.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".