Bibliographic record
Abstract
The Radiation Effects Data Workshop (REDW) is part of the Nuclear and Space Radiation Effect Conference (NSREC) Technical Program. It is held as a separate poster session with a separate Workshop Record Publication. Posters were presented during the 2007 NSREC held at the Hilton Hawaiian Village, Honolulu, Hawaii, on July 25. The purpose of the REDW is to make available to the radiation effect community high quality radiation effects data. It also provides descriptions of radiation effects test facilities, standards, and environments. The Workshop Record published each year is a permanent archive of the REDW. The 2007 Workshop Record has thirty-nine high quality papers covering a broad range of topics including: total ionizing dose, displacement damage, and single particle effects on a large number of electronic devices, integrated circuits and detectors; test methodologies; environment and facilities. In addition, like the conference, there is substantial international participation that includes presentations from Canada, England, Finland, France, and Spain, as well as the United States. This Workshop Record contains the largest number of papers published to date. Although the Workshop Record provide a cumulative index that can be used to locate papers based on author and title, it is difficult to search for response data on a particular part number, type, or radiation effect. To simplify this activity searchable tables covering the Workshop Records 1992-2005 were prepared and published in 2006 Workshop Record. This year, a searchable table covering 2006 Workshop Record is presented.
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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.502 | 0.351 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".