Bibliographic record
Abstract
Pharkya, a Ph.D. candidate in materials science and engineering, works in the area of corrosion science, predicting how materials will perform over extended periods of time. Her particular focus is a nickel-chromium-molybdenum alloy called C-22, a highly corrosion-resistant metal. Pharkya's aim is to help determine whether containers made from C-22 can be used to store high-energy nuclear waste--for 10,000 years and longer. Pharkya's work is part of a plan by the U.S. Department of Energy to consolidate the country's nuclear waste in a single proposed repository. The proposed repository is in Yucca Mountain located in a remote Nevada desert. Currently about 70,000 metric tons of spent nuclear fuel and high-level radioactive waste are divided between approximately 100 sites around the country. The undertaking, Pharkya emphasizes, is massive. To study just the corrosion aspects of the packaging, Case is collaborating with eight other universities, five national labs and Atomic Energy of Canada Limited. Even with so many players, the study will likely take several years to complete. Heading the entire group is Joe Payer, a professor of materials science and engineering at Case and Pharkya's mentor. ''I came here to have the opportunity to work with Dr. Payer, an expert in corrosion, but I didn't know specifically what I would be working on'', Pharkya recalls. ''I was pretty thrilled when I learned about the vastness of the project--my research would be just a small part of this huge topic--and the impact of the research we would be doing''.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.352 | 0.263 |
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".