Bibliographic record
Abstract
There's Plenty of Room at the Bottom: An Invitation to Enter a New Field of Physics" articulated a vision of miniaturization in which fantastic mechanisms and processes could be realized. These included the creation of miniature swallowable surgical robots and arrays of macroscale machines that themselves create smaller scale machines, eventually leading to massively parallel microscale factories. Though scholars debate the direct impact of Feynman's talk on the development of micro/nanotechnology, no doubt the ensuing six decades have seen tremendous advances in the evolution of this vision. In the last three to four decades in particular we have witnessed tremendous progress in the advent of microscale thermal-fluid systems, including the development of microscale cooling mechanisms for microelectronics, "Lab-on-a-chip" technology for chemical and biological assays, DNA amplification, microscale heat exchangers, and micro-shocktubes to name just a few. In addition to the explosion of creativity and innovation sparked by the lack of off-the-shelf solutions at the microscale, we have experienced a renaissance of sorts of otherwise well-established fields/theories in thermal-fluids. In this talk, we will explore some of the milestones of microscale thermal-fluids so far as well as discuss the tremendous opportunities still awaiting us, not only in terms of microfabrication paradigms, but alsoand perhaps more importantly-new approaches to analysis and modelling steeped in the fundamentals. Indeed, there is still plenty of room at the bottom.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".