Commercial Opportunities and Market Demand for Nanotechnologies in Agribusiness Sector
Bibliographic record
Abstract
Agribusiness is projected to be a $ 2.9 trillion USD industry in global investment by 2030 (World Bank 2013).Nanotechnology is poised to impact dramatically on all sectors of agribusiness industry in the next 10 years.Nanotechnology could be used to enhance the possibilities of developing conventional and stranded agribusiness resources.Nanotechnology can make the industry considerably greener and competitive, with its current growth rate of 25% (US$ 1.08billion) annually.The opportunity for application of nanotechnology in agricul¬ture is prodigious.Nanotechnology, focusing on special properties of materials emerging from nanometric size has the potential to revolutionize the agricultural and food sectors, biomedicine, environmental engineering, safety and security, water resources, energy conversion, and numerous other areas.It is well recognized that adoption of new technology is crucial in accu-mulation of global wealth and market value which now stand at US$ 1.09 trillion in estimated value.Nanotechnology has emerged as a technological advancement that could develop and transform the entire agri-food sector, with the potential to increase agricultural productivity, food security and economic growth for industries by atleast 30% (Aver.US$0.9 trillion).This review set out to address the implications of nanotechnology for the agri-food industry by examining the potential benefits, risks and opportunities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".