Bibliographic record
Abstract
Future of nanotechnology In 2000, Roco et al . [1] estimated that there would be two million nanotechnology workers worldwide (800 000 in the United States) and the product value would reach $1 trillion, of which $800 billion would be in the United States, by 2015, with a 25% rate growth. The initial estimation for the quasi-exponential growth in the nanotechnology workforce and the product value held up to 2008, as shown in Fig.10.1. The market is doubling every three years as a result of the successive introduction of new products, and new generations of nanotechnology products are expected to enter the market within the next few years [1]. So the estimated value of 2015 for both workforce and product value would have been realized as the 25% growth rate is expected to continue. Nanotechnology is evolving toward new scientific and engineering challenges in areas such as assembly of nanosystems, nanobiotechnology and nanobiomedicine, development of advanced tools, environmental preservation and protection, and pursuit of societal implication studies. Key areas of emphasis in nanotechnology [1] over the next decade are as follows. Integration of knowledge at the nanoscale and of nanocomponents in nanosystems with deterministic and complex behavior, aiming toward creating fundamentally new products. Better control of molecular self-assembly, quantum behavior, creation of new molecules, and interaction of nanostructures with external fields in order to build materials, devices and systems by modeling and computational design. Understanding of biological processes and of nanobio interfaces with abiotic materials, and their biomedical and health/safety applications, and nanotechnology solutions for sustainable natural resources and nanomanufacturing.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".