Radioactive waste management and contaminated site clean-up : processes, technologies and international experience
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it