Bibliographic record
Abstract
You never feel quite as comfortable about a site as the day you start to study it. Wendell Weart On June 4, 2008, at 8:40 a.m., a moving truck pulled up to the Nuclear Regulatory Commission and dropped off 15 sets of the Department of Energy's license application to construct a nuclear waste repository at Yucca Mountain. Each set weighed 110 pounds (50 kg) and totaled 8,600 pages. It was more than a quarter-century since work began at Yucca Mountain and a decade after the federal government had promised the nuclear industry an operational repository for their wastes. This delivery marked an important milestone, yet it would be at least another decade until the repository opened – perhaps never, if Nevada had its way. Comprising hundreds of studies, the license application was the most complex application ever completed. Never before had mere mortals been assigned the task of making scientific predictions spanning such geologic timeframes. total system performance assessment A good part of the case presented to the NRC was based on a Total System Performance Assessment , or TSPA. Using a series of linked computer models, the TSPA strove to provide quantitative answers to three basic questions: What might happen in the future to the waste? How likely are different scenarios? What are their consequences? The TSPA was the grail of this difficult and extended quest. The effort was led by Sandia National Laboratories, world leaders in performance assessment.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.062 | 0.025 |
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".