KTH Industrial Engineering and Management NONLINEAR ISOVISCOUS BEHAVIOUR OF COMPLIANT JOURNAL BEARINGS
Bibliographic record
Abstract
God. I give my deepest gratitude to my wife, Woori. She has given me a big support and love. And she waited patiently for me to complete my thesis and taking care of our two lovely children. I would also like to thank my daughter, Ye-Eun for growing up healthy and bringing joy to my family. I would like to welcome my newly born son, Ye-Lang, to my family. I want to thank both of my parents and families in Korea and Canada for their care, love and support. I would like to thank my supervisor, Professor Sergei Glavatskih for giving me an opportunity to study and research under his guidance. He has brought me into the field of fluid film bearings and there are many things to learn and absorb from his expertise. His valuable discussion, support and help are gratefully acknowledged. Without his supervision and friendship, this work was not possible to accomplish. I would also like to thank my colleagues from Luleå University of Technology
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".