Genome Editing Against Bacterial Plant Pathogens
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.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".