Minichromosomes: The second generation genetic engineering tool
Bibliographic record
Abstract
Genetic engineering is a scientific tool used in every field of science like plant, animal and human sciences. Plant genetic engineering technology has changed the face of plant sciences and the first generation of transgenic crops has become the most rapidly adopted technology in modern agriculture. But genetic engineering has some limitations and therefore still there is a clear need of new technologies to overcome issues like gene stacking, transgene position effects and insertion-site complexity. The recent strategy that researchers have developed to overcome those limitations is the development of plant artificial minichromosomes for delivery of large DNA sequences, including large genes, multigene complexes, or even complete metabolic pathways. A minichromosome is an extremely small version of a chromosome that have been produced by de novo construction using cloned components of chromosomes or through telomere-mediated truncation of endogenous chromosomes. After a successful experiment in maize with the help of minichromsomes by J. Birchler and colleagues (Yu et al., 2007a), a new paradigm have been set for all the agricultural researchers to use the minichromosome techniques for crop improvement. Engineered minichromosomes also offer an enormous opportunity to improve crop performance, as discussed by Houben and Schubert (2007). With rapidly expanding research in minichromosome as a second generation genetic engineering tool we can hope that it will bring a new generation of improved crop species to meet the global demands.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.004 | 0.005 |
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".