Bibliographic record
Abstract
As an advanced gene-editing tool, CRISPR technology has shown tremendous potential in the field of endangered species conservation in recent years. This study introduces the primary applications and challenges of CRISPR technology in endangered species conservation. First, CRISPR technology improves genetic diversity, health level, and disease resistance of endangered species through gene repair, the introduction of new genes and controlling the spread of harmful mutations. In addition, CRISPR gene drive technology effectively controls invasive alien species and reduces competitive pressure on native endangered species. CRISPR technology can also enhance the adaptability of endangered species to environmental pollution and climate change, such as by introducing anti-pollution genes and regulating heat tolerance genes to improve adaptability. However CRISPR technology faces technical and ethical challenges in endangered species conservation. On the technical level, the lack of genomic data for non-model organisms and off-target effects of gene editing are the main issues. On the ethical level, gene editing may alter the natural evolutionary process of species and bring ecological risks. Therefore, interdisciplinary collaboration needs to be strengthened to ensure that scientists, policymakers, and the public jointly explore solutions. Looking ahead, improved gene-editing tools and advances in bioinformatics will enhance the accuracy and efficiency of gene editing, promote data sharing and interdisciplinary cooperation to advance CRISPR technology in endangered species conservation. Through technological improvements and multi-stakeholder collaboration, CRISPR technology will play a significant role in global biodiversity conservation.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".