Bibliographic record
Abstract
Abstract Targeted genome editing by Clustered Regularly Interspaced Short Palindromic Repeat- CRISPR-associated (CRISPR-Cas) system has revolutionized basic and translational plant research. There is widespread use of CRISPR-Cas technology which has the potential to address challenges like food insecurity and climate crisis. Crops with improved traits (e.g., higher yield, drought tolerant) that would take several years to generate can now be developed at a much reduced time, drastically expediting the availability of the crops for release in the market. However, several factors are involved in successfully applying the CRISPR-Cas system in agriculture and the widespread adoption and acceptability of genome-edited products that involve multiple institutions and people from different spheres of society. Besides the scientific and legal intricacies of releasing CRISPR-edited crops, “public perception” equally matters in successfully deploying the technology and its products. “Lack of” or “overwhelming” information can both affect the success of the CRISPR-Cas system in translational agriculture research. A bird’s-eye-view of the CRISPR-Cas genome editing tool for people from different strata of society is essential for the wide acceptability of genome-edited crops. This review provides a general overview of the CRISPR-Cas system, the concept of technology development, challenges, and regulations involved in translational research. Graphical abstract
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".