Genome Editing Against Bacterial Plant Pathogens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Meeting the crucial demand for sustainable agriculture is an upcoming challenge worldwide, leading to global food security concerns. Approximately 50% of agricultural loss is caused by both biotic and abiotic stresses. As per the estimation of Agrios, 42% of crop loss is characterized by biotic stress alone. Bacteria are the second largest contributor in terms of economic losses caused by various plant diseases. Hence, there is a need to develop elite cultivars in amalgamation with readily available sequenced plant database and progressive genome editing. This has proved to be a groundbreaking/milestone in the field of plant breeding for any desired trait. Until now, many new plant breeding techniques (NPBTs) have been introduced for crop improvement. These techniques include site-specific mutagenesis, cisgenesis, intragenesis, breeding with transgenic inducer lines, etc. This book chapter provides a comparative understanding of enrichment in plant genome editing approach about bacterial pathogens aiming for sustainable agriculture development. This chapter also brings a broad aspect of the application, advantages, unsighted aspects of genome editing, and future challenges.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it