Fabrication of Surface Plasmon Biosensors in CYTOP By
Bibliographic record
Abstract
I would like to thank my parents and brother first and foremost for their support and guidance throughout the years and for their patience while I completed my studies. I would also like to thank all the contributors to my research, particularly my thesis supervisors, Dr. Pierre Berini and Dr. Niall Tait, for their direction and for giving me the opportunity to complete the works described herein. Furthermore, I would like to thank the technical support staff at the University of Ottawa, Ewa Lisicka and Michal Tensor, for their assistance and expertise in materials processing and device testing. I would like to thank my predecessors and colleagues, Charles Chiu, Raza Haniff, Norman Fong, Alex Krupin and Asad Khan for their hard work and assistance with my assimilation into the project. Finally I would like to thank the Carleton University Micro-fabrication lab personnel, Rob Vandusen, Rick Adams and Angela Burns for their assistance in fabrication techniques and equipment usage as well as providing me with the necessary training to carry out my research. II This thesis describes work carried out on the research, development and
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".