Bibliographic record
Abstract
We live in an era of seemingly unending technological “breakthroughs,” “game changers,” “next big thing,” and “paradigm shifts.” Medical “breakthroughs” appear to grow exponentially every day. Many of these revolve around immunotherapeutic drug treatments for curing cancer. Yet escalating costs versus benefits is a major, but often glossed over, concern. Nanotechnology is beginning to provide medical research teams with new, potentially more cost-effective, and scalable nano-based treatment modalities for global application. For this to materialize, the interplay between various academic researchers, clinicians, industrial, and regulatory bodies will require an effective communication effort that fuses the strengths of various disciplines, encompassing chemistry, physics, biology, engineering, and medicine into a secure knowledge base from which to draw upon. To continue to advance, nanotechnology must emphasize interdisciplinary collaboration based on a systems-based approach. This chapter concludes with a brief examination of two examples of types of innovative solutions that employ this strategy: the Symbiosis program launched in 2017 by Science World in Vancouver, Canada, and the Advanced Science Research Center at the City University of New York in Manhattan, New York.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.018 |
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".