Virtual broker system to manage research and development for micro electro mechanical systems
Bibliographic record
Abstract
This works presents an analysis of the need for efficient managerial tools in order to address various challenges and opportunities for the micro and nano-electro-mechanical-systems (MEMS/NEMS) industry, and to expedite the development cycle and shorten the total time from idea to market for devices based on these technologies. A methodology to provide support to the MEMS/NEMS community (i.e., researchers, designers, and entrepreneurs) is proposed and described. This methodology offers guidance during the early stages of MEMS/NEMS product development, provides means to manage research and development, and acts as a virtual broker in order to coordinate collaboration among various organizations to optimize the use of existing fabrication infrastructure. Innovative products based on MEMS/NEMS have made rapid improvements in terms of functionality, cost, performance, etc. However, many applications and devices based on these systems are still in the research phase, struggling to reach to a commercial stage.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".