Bibliographic record
Abstract
People&s;s needs and desires for the diversity of food and food products have compelled plant breeders to invent proficient technologies for precise genetic modification such as genome editing (GE) for rapid modification of traits which otherwise would take millions of years under natural conditions. The technologies that include the use of site-directed nucleases technologies, oligonucleotide-directed mutagenesis, zinc-finger nucleases, meganucleases, clustered regularly interspaced short palindromic repeats-Cas, transcription activator-like effector nucleases, etcetera, revolutionize crop development, enable rapid improvement of qualitative and quantitative traits, and add to the diversity of crops and crops products to meet the needs of a growing population with its increased preferences and purchasing capacities. GE products face policy obstacles and uncertainties owing to acceptance or rejection by consumers. Countries like the United States of America, Canada, Argentina, Brazil, etcetera, have developed policies and strict regulatory mechanisms for the commercialization of GE crops. The European Union and a few other countries have stricter regulations. While the technology has leaped from a transgenic genetic modification (GM) system to a cisgenic system of GE, the consumers’ and critics’ knowledge revolve around the GM debate that has created hurdles in the commercialization of GE crops. This chapter contains scenarios of GE crops and necessary recommendations.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".