Radioactive waste management and contaminated site clean-up : processes, technologies and international experience
Bibliographic record
Abstract
Part 1 Background and principles of radioactive waste (RAW) management: Fundamentals of radioactive waste (RAW): Science, sources, classification and management strategies Radioactive waste (RAW) categories, characterisation and processing route selection International safety standards for radioactive waste (RAW) management and remediation of contaminated sites Technical solutions for the management of radioactive waste (RAW): Overview and methods of selection Irradiated nuclear fuel management: Resource versus waste Radioactive waste (RAW) conditioning, immobilisation and encapsulation processes and technologies: Overview and advances Assessing and modelling the performance of nuclear waste and associated packages for long term management Remediation of radioactively contaminated sites and management of the resulting waste Safety and risk assessment of radioactive wastes and contaminated sites. Part 2 Current international situation: Experience of radioactive waste (RAW) management and contaminated site clean-up Russia Ukraine Czech Republic, Slovak Republic and Poland Nordic countries Germany France England and Wales Scotland United States Canada South Africa Republic of Korea China Japan Fukushima: The current situation and future plans. Part 3 Clean-up of sites contaminated by weapons programmes: Management of radioactive waste (RAW) from nuclear weapons programmes Modeling and strategy approaches for assessing radionuclide contamination from underground testing of nuclear weapons in Nevada, USA Remote monitoring of former underground nuclear explosion sites predominantly in the former USSR.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".