Market analysis and microbial biopreparations creation for crop production in Ukraine
Bibliographic record
Abstract
BIOTECHNOLOGIA ACTA, V. 8, No 4, 2015Microbial Biotechnology are an integral part of modern innovative technologies that have found their application in industry, medicine, pharmacy, water management, agro-industrial production. According to Frost and Sullivan [1], the volume of the global biotechnology market in 2013 is estimated at 270 billion US dollars and projected growth rate until 2020 will be up to 10–12% per year, i.e. the global market for biotechnology will approach 600 billion dollars. According to experts, the global biotechnology market in 2025 will reach 2 trillion United States dollars, and the growth of individual segments of the market will be up to 30% [2]. Segmentation of the global biotechnology market is as follows (Frost and Sullivan, 2014) [1]: the main share (60%) is in biopharmaceuticals and biomedicine (the so-called “red biotechnology”), the share of industrial biotechnology and bioenergy (“white biotechnology”) is 35%. The agricultural and environmental (“green”) biotechnologies are 5%. The last segment of the market is actively developed in the US, Europe (France, Germany, Denmark, Switzerland, Sweden), Canada, Australia, Japan and Israel. Growing in the last 5 years, biotech markets, including agrobiotechnological market, are typical for China, India, Brazil, Argentina.A significant part of agricultural biotechnology is associated with microbial biopreparations for crop production that is one of the components of ecological (organic)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".