Bibliographic record
Abstract
The goal of this thesis is to explore the use of Micro-Electro-Mechanical Systems for biochemical analysis. The work consists of two projects; the first deals with the fabrication and testing of MEMS a die, the second an application oriented investigation providing incremental steps towards establishing an environment for a lab-on-a-chip research group. The first project relates to the fabrication of MEMS structures, and is an investigation of a low-cost post processing lab to determine if an in-house post processing environment for standard CMOS processes using KOH etching would be beneficial and feasible. This project also links available Canadian Microelectronics Corporation (CMC) supported fabrication processes (Mitel 1.5mum CMOS) to a commercial CAD package (IntelliCAD from Intellisense Inc.). For the second project in the thesis we have taken two specific examples; a DNA replication system and a MEMS electrophoresis technique used to separate organic material using an electric field. The DNA replication technique to be used is referred to as Polymerase Chain Reaction (PCR). (Abstract shortened by UMI.)Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .B78. Source: Masters Abstracts International, Volume: 40-03, page: 0751. Thesis (M.A.Sc.)--University of Windsor (Canada), 2001.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".