Problems with Radioactive Sources in Recycled Metals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div class="htmlview paragraph">Since 1983, there have been at least 65 confirmed, reported events where radioactive materials were inadvertently mixed with metals for recycling, and in many of these instances, radioactively contaminated metal resulted. The problem is worldwide, with the iron/steel industry and the aluminum industry being the most seriously affected, but other industries have also suffered. Despite the widespread use of radiation detectors (“portal monitors”) by recycling industries, radioactive sources do slip through, and can cause severe economic impacts if a source is breached or melted. In North America, over 350 radioactive sources have been caught before a melting occurred, but there have been 32 meltings in the United States and Canada alone. The problem has caught the attention not only of the Conference of Radiation Control Program Directors, Inc. (CRCPD), but also of the US Nuclear Regulatory Commission, the US Environmental Protection Agency and other members of the federal family. Efforts are underway to prevent orphan radioactive sources from being recycled inadvertently or illegally, which will be detailed at this conference. CRCPD has established assistance for dealing with the problem of radioactive scrap and with the disposition of unwanted radioactive material.</div>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it