Radiological impact assessment approaches for Life Cycle Assessment: a review and possible ways forward
Bibliographic record
Abstract
Many industrial processes routinely release radionuclides into the environment. Such emissions may be recognised in the inventory phase of Life Cycle Assessment (LCA), but they are rarely carried forward to the Life Cycle Impact Assessment (LCIA) phase because a standard approach for assessing their impact is still lacking. The aim of this article is to collect and critically analyse radiological impact assessment methodologies to establish a basis for developing a standard approach. Seven methodologies are reviewed. Amongst these, the human health damages approach is the only methodology to date to be included in LCIA methodologies. Furthermore, five of the reviewed methodologies are concerned with impacts on humans, whilst the remaining two address effects on the environment. The article concludes that even though a number of methodologies are currently available, none is suitable as the basis for a standard procedure in LCIA. Two main features have been identified as crucial: the ability to treat all types of waste forms by which radionuclides can be released and the use of a fate analysis that returns average (rather than worst case) estimates of impacts. In light of the findings of this review, a novel framework for radiological impact assessment on humans has been devised; its development is being pursued by the authors.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".