Bibliographic record
Abstract
Abstract The miniaturization of mechanical systems, and more specifically micromechanical devices, promise new opportunities in many biomedical applications because of their specific characteristics. These micromechanical devices are much smaller, lighter, faster (e.g., higher resonant frequency), and often more precise or sensitive than their macroscopic counterparts. In particular, micromechanical devices present opportunities in assisting in diagnostic, surgical, and therapeutic applications. Micromechanical devices are also often referred to as micro‐electromechanical systems (MEMS) when combined with electronics, and when conceived for a particular biotechnology‐based application, they are often referred to as bioMEMS (bio‐micro‐electromechanical systems). Today, micromechanical devices exist in many environments such as automotive, consumer, industrial, aerospace, and biomedical. Biomedical applications for micromechanical devices are an area that is forecast to experience substantial growth. An example is the implantable and disposable blood pressure sensor, one of the earliest bioMEMS applications, which continues to grow. MEMS‐based lab‐on‐a‐chip for point‐of‐care diagnostics on a patient is another promising application in biomedical where the time and cost associated with conventional methods can be reduced significantly. Of particular interest in the biomedical field are micromechanical devices such as microtransducers in the form of micromechanical sensors and micromechanical actuators including micromotors.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.040 |
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".