Bibliographic record
Abstract
The members of the Committee appointed to examine the thesis of NAVARATNARAJAH SASIHARAN find it satisfactory and recommend that it be accepted. ii Chair ACKNOWLEDGEMENT First of all, I would like to express my great appreciation to Dr.Balasingam Muhunthan for his irreplaceable encouragement, guidance and support throughout this study. He has been an inspiration to me since I applied for admission to Washington State University. A person of myriad skills, he has eased the way of my work by his wonderful contribution. He volunteered many hours of his valuable time to help me put my best foot forward. I would also like to express my thanks to Mr. V.S Pillai, Geotechnical Engineer, Vancouver, B.C, for being a veritable wellspring of ideas and suggestions pertaining to this research. His vast knowledge on dam construction and experience made this project possible and even more enjoyable. Also, I wish to thank Dr. Adrian Rodriguez-Marek and Dr. William Cofer for their assistance in this study. My gratitude also goes to my colleagues in GeoTransportation group, especially Mr. Omar Al-Hattamleh and Sathish Balamuragan for helping me in many ways. Financial support by the National Science Foundation (Grant CMS-0234130) and Washington State Department of Transportation is acknowledged with gratitude. Last but certainly not least, I would like to express my deepest gratitude for the constant support, understanding and love that I received from my wife Lojini and my family during the this study. iii
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.378 | 0.260 |
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".