Solution Processable Semiconductor Thin Films: Correlation Between Morphological, Structural, Optical and Charge Transport Properties
Bibliographic record
Abstract
During the past five years, I have had the opportunity to interact with many invaluable colleagues at École Polytechnique de Montréal and with collaborators from other universities.Along the way, I have had the chance to build strong relationships, which I believe will be carried into the future.The process of obtaining my Ph.D. degree in Canada was an exquisite and rewarding experience, which I believe not only contributed to my scientific background and adaptation to a multicultural environment but also helped me in improving my interpersonal relationship and my leadership skills.Having interacted with a large group of people I owe many "Thank you!" for all the support I have received.This Ph.D. thesis would not have been possible without the guidance of my supervisor Prof. Clara Santato.During the past five years, she was always there for me as a mentor to ensure high quality in our research.I would especially like to thank her for helping me realize my dream towards obtaining my Ph.D. and become an independent researcher and a scientist.I cannot fail to mention that she was always there to lend an ear when I needed one.I have been lucky with all the opportunities given to me for improving and developing my scientific skills.I am also proud to be the first Ph.D. student
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".