The Impact of Hydrogen and Oxidizing Impurities in Chemical Vapor Deposition of Graphene on Copper
Bibliographic record
Abstract
The completion of this thesis has been an amazing journey filled with many experiences from graphene growth to intellectual growth!I had the opportunity to work under the direction of truly astonishing individuals: Patrick Desjardins and Richard Martel.Thank you for accepting me into your group and providing me your guidance and support.Dear Patrick, there have been numerous occasions where I remember feeling disheartened and skeptical about the path that I am taking, but inevitably, a meeting with you has refreshed my enthusiasm and raised my spirits immeasurably.Your dedicated support and guidance have been invaluable over the past five years and I feel incredibly privileged to have you as my supervisor.Needless to say, I enjoyed every minute of our discussions and I will long remember those days.Dear Richard, your wisdom, knowledge and exceptional passion for science has inspired me and enriched my growth as a student and researcher.Your enthusiasm in every stage of this project energized me and helped me to stay committed to attain excellence.I am extremely honored to work under your supervision.I would like to
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".