The Emergence of the Nanobiotechnology Industry
Bibliographic record
Abstract
Abstract: The confluence of nanotechnology and biotechnology provides significant commercial opportunities. By identifying, classifying and tracking firms with capabilities in both biotechnology and nanotechnology over time, we analyze the emergence and evolution of the global nanobiotechnology industry. Research in nanotechnology has expanded rapidly in the last 15 years, but the development of commercial products has been significantly slower1-3. One of the most promising areas for commercialization is the application of nanotechnology to biological processes4-6. This is due, in part, to the fact that it involves the confluence of two previously disparate research fields – nanotechnology and biotechnology – and novel combinations of ideas and approaches are known to increase the opportunities for innovation7,8. The rate of increase in nanobiotechnology invention is well documented9,10. However, little is known about the commercialization of these inventions and the entry of firms into the field. For example, what types of companies have both biotechnology and nanotechnology capabilities, when and where did they develop them, and what applications are they targeting? And are these companies actually integrating nanotechnology with biotechnology? Here we explore the
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".