Bibliographic record
Abstract
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.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".