Bibliographic record
Abstract
World population has already exceeded 6.5 billion, out of which about 850 million (13 percent) are undernourished. With the current growth rate, the world community faces even a greater challenge of hunger and food security as the estimated the population will catch 9 billion by the year 2050 with doubled needs for food. At the same time, preservation of biodiversity, stopping deforestation and reduced environmental footprint caused by agricultural practice are the main concerns towards sustainable agriculture. The progressive adoption of genetically modified organisms (GMOs) including GM crops and trees can make a decisive contribution to improve harvest and alleviate hunger and poverty. In addition to the environmental benefits, the introduced GMOs can improve water use efficiency and reduce the need for fossil based fuels and pesticide application and reduce thousands of tons of emissions of green house gases. In this respect, several social issues are still of concern. On one hand, many beneficial advantages of GMOs have encouraged a wide spectrum of large or small farmers to cultivate transgenic plants which is translated to food security and job opportunities. On the other hand, while ideological debates have hindered, or even ceased, technology provisions in developing and underdeveloped countries, GMO seed and food productions are monopolized by a quite small number of transnational companies. For instance, seeds that were previously available at low or no cost, mainly through public institutes, international entities or seed exchange among farmers, would be offered at higher prices due to exclusive right of producers and additional cost of patent royalties. Cartagena protocol is going to regulate the relationship among countries aiming at preservation of world biodiversity. Although the developing countries outnumbered developed nations that cultivate transgenic plants in recent years, current statistic shows that around 84 percent of GMO crops are cultivated by only four countries, USA, Brazil, Argentina and Canada. Scientist and scholars, particularly in Islamic states, as well as decision makers are the major responsible bodies that must take roles for the current and future situations. Despite the fact that most of scientists or scholars are not in navigation positions, however, they can discuss socio-economic issues and raise public awareness in order to harmonize their efforts towards proper utilization of biotech products in their society and towards a reliable point for food security and safety.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.057 | 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".