Management Status of Biotechnology Industry in Europe :::union:::, America and Canada and Provide Practical Solution for Iran
Bibliographic record
Abstract
Aim: Medical biotechnology is one of the newest fields of biotechnology which has been underlie the dramatic changes in diagnosis and treatment of various diseases. This topic is a new plan in our country. The purpose of this study is to review the most important strategies of Europe :::union:::, America and Canada and provide practical solution for Iran. Methods: This is a non-systematic review study which has been conducted in 2016. This review was based on Scoping Review methodology. Studied indicators in this study include economic and educational. Data was collected through review of library texts, internet searches and direct referral to the relevant organizations. Results: The income of biotechnology companies have increased 80 percent and the costs of R&D in these centers have increased 5 percent. The remarkable point is the net income growth of 37 percent in 2012 compared to last year. Results shows that commercial leaders ahead of public & other companies in Financial indices. The status of biotechnology industry in The US was observed better than Canada and Europe :::union:::. Regarding the status of educational indices in the biotechnology industry, the number of MBA subjects seems more than the number of biotechnology and medical biotechnology subjects. Conclusion: Support the establishment of private institutions and encourage them to look at the knowledge gained from biotechnology with economic, industrial and global marketing thinking as well as acceding to the global laws of intellectual property and global trade can be underlie entering the biotechnology industry arena and global market which the experts of this industry and technology will play an effective in this arena
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".