Multiscale modelling of gas hydrate mechanical and thermal properties
Bibliographic record
Abstract
I would like to express my gratitude to all my friends who made my time as a graduate student much easier with their company and the smiles we shared.The priceless time I spent with Fatou and Zainab was the best bit of every week.The long hours of laughter with Zeina helped me get through the times of stress, and Serene's serenity was essential especially during our encounters with the French language.The moments I spent with my caring friend Zulikha and her lovely family charged me with tranquillity despite my hectic schedule.Also, my colleagues in my research group were of great support: Pardis with her encouragement and exceptional sense of humor, the Oscars and the friendly atmosphere and interesting conversations they brought in, and Hang who helped me with installing software.Thanks are also due to François for translating the thesis abstract.v A huge thank-you is due to the ones who have prepared me to venture this academic path in the first place.Whether knowingly or not, Prof. Naif Darwish and Prof. Rachid Chebbi have given me invaluable insight on what it takes to become a great, influential professor with their high ethics, exceptional knowledge and wisdom, and caring nature.They were and will always be my primary role models in academia.Lastly and most importantly, the deepest acknowledgement goes to my family, grandparents, and relatives who made this possible and followed me throughout the process.Words cannot express how grateful I am to be part of such a loving family which supported me on all levels, and I cannot help but thank each one of them: my Mom, Reem, for her endless love and confidence in me; my Dad, Mustafa
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".