Medical and pharmaceutical nanoengineering conference/International conference on MEMS, nano and smart systems
Bibliographic record
Abstract
Researchers representing all the northern hemispheric continents gathered for 3 days in Banff, Canada, to hear a wide range of talks on the application of micro- and nanotechnology to drug delivery. Topics included nanotubes, nanoparticles, liposomes, micelles, novel inhaled aerosols, antibody engineering and vaccines. Also featured were talks on the application of micro- and nanotechnology to diagnostics, including microfluidics, as well as biomolecular computing. The conference showcased the first demonstration of preliminary concepts for "smart particle aerosols", in which nanofabrication methods are used to produce inhaled aerosol particles with "intelligent" features. The conference provided an excellent forum for cross-fertilisation and discussion between disciplines, with attendees covering a broad range of areas in engineering and the physical and life sciences that are not often found together at a single conference. This breadth of attendees and topics provided a highly stimulating environment. An invitation to next year's conference (24-29 July 2005, Banff, Alberta, Canada) was extended, as listed at the conference website [101].
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.021 |
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".