Genome Engineering and Essential Mineral Enrichment of Crops
Bibliographic record
Abstract
Crops lacking in essential minerals are a main cause of nutrient deficiencies in humans. Malnutrition affects a large proportion of the global population, due to low concentrations of micronutrients found within the edible portions of crops. One approach to address this is to increase the mineral concentrations within edible crops. Genetic variation within crop varieties can be exploited to improve biofortification through plant breeding. Genetic engineering is a promising technology to address the problem, and gene editing in particular is a precise method now used to develop nutrient rich crops. A number of genetically modified biofortified plant cultivars with increased mineral content are now available, and new genotypes with higher mineral concentrations are currently under development. Root engineering of cereals is a recent approach used in biofortification, stress tolerance and phytoremediation through the genetic engineering of crops. This emerging new era of transgenic plants offers a promising future to combat health problems related to nutrient deficiencies, as well as to address global environmental pollution.
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 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".