Transgenic: Why should their Adoption and Consumption in Nigeria be taken with Cautiousness
Bibliographic record
Abstract
Genetic Engineering which involves the removal of genetic material from one organism and splicing it into the chromosomes of another is today set to revolutionize agriculture. It has given rise to a new set of organisms known as Genetically Modified Organisms (GMOs or Transgenic). The major advantage of GMO crops are yield increases as well as reduction in pesticide and herbicide use. Genetically Modified crops are today flourishing across the globe particularly in five leading countries namely the US, Argentina, China, Canada and Brazil. Worldwide, 181.5 million hectares were planted with GMO crops in 2014 with the US accounting for 40.28% of that average. About 5% of all canola, 13% of all corn, 31% of all cotton and 51% of soybean grown across the world today are genetically engineered. Notwithstanding their high potential caution need to be exercised in the adoption and consumption of GMO crops in Nigeria. Their health and environmental implications are yet to be subjected to long term scientific investigations. Fallouts from past scientific discoveries give credence to this call. For instance, nobody knew at the time DDT was discovered that DDT sprayed over a broad area would be bio-magnified through the food chain and concentrated hundreds of thousands of times in the human body. When CFCs were created, they were hailed as great discovery - inert compounds that were great carriers for aerosol sprays. Only when millions of tons of CFCs were liberated into air many years later was their scavenging effect on ozone in the upper atmosphere discovered. This paper therefore aims not only to highlight the benefits derivable from this new technology but also the need to exercise caution in the adoption and consumption of GM crops in Nigeria.
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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 | 0.002 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".