Research and Development of Lead Block Cutting Technique for Decommissioning Nuclear Power Plants
Bibliographic record
Abstract
Nuclear power plants and research facilities that use radioactive materials employ lead to shield against radiation. Lead must be cut to an appropriate size before being removed from the facility as waste. Because lead is soft, it is difficult to cut using mechanical cutting methods such as reciprocating saws. In this study, we attempted to make lead into a low-melting-point alloy of bismuth and tin (eutectic point 95 °C) and devised a new cutting method that combines mechanical cutting and low-melting-point alloying. For the cutting experiment, we used a Bi-Sn alloy (eutectic point 139 °C) that we prepared and a lead block (50 mm thick). To react the Bi-Sn alloy with the lead block, it was necessary to heat the lead block to 139 °C or higher. Therefore, we conducted a temperature increase experiment on the lead block using a heating wire with thermal conduction and an infrared heater with radiation. The results showed that heating with an infrared heater was superior. The molten Bi-Sn alloy was brought into contact with the heated lead block to make a low-melting-point alloy. Here, a reciprocating saw was used to successfully cut the lead block and remove the low-melting-point alloy that had formed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".