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Record W4281257767 · doi:10.1088/1361-6498/ac6045

Approaches to the definition of potentially exposed groups and potentially exposed populations of biota in the context of solid radioactive waste

2022· review· en· W4281257767 on OpenAlexaff
Lise Griffault, Emilie Aubonnet, J. B. Brown, Reda Guerfi, Ulrik Kautsky, Raymond Kowe, Peter Saetre, Shibutani Sanae, G. M. Smith, Karen L. Smith, M C Thorne, Russell Walke

Bibliographic record

VenueJournal of Radiological Protection · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsNuclear Waste Management Organization
FundersInternational Atomic Energy Agency
KeywordsBiosphereContext (archaeology)Radioactive wasteEnvironmental scienceBiotaMunicipal solid wasteBiomass (ecology)Identification (biology)Environmental planningRisk analysis (engineering)Waste managementEnvironmental resource managementEngineeringEcologyBusinessBiology

Abstract

fetched live from OpenAlex

A methodology for addressing the biosphere in safety assessments for solid radioactive waste disposal was developed through theme 1 of the IAEA coordinated research project on BIOsphere Modelling and ASSessment (BIOMASS) that ran from 1996 to 2001. This methodology provided guidance on how the biosphere can be addressed in safety assessments for disposal of solid radioactive waste. Since the methodology was developed, it has proven useful and has been widely referenced in assessments in a diversity of contexts encompassing both near-surface and deep geological disposal of solid radioactive waste. The principles that could be adopted for defining potentially exposed groups (PEGs) were an important aspect in the original BIOMASS methodology as the endpoint of an assessment usually includes the evaluation of individual dose or risk to human health. Identification of PEGs and definition of their characteristics are usually made to be consistent with the biosphere system description being developed, acknowledging that due to inherent uncertainties in projecting future human behaviour, the biosphere models adopted for assessing safety of a disposal system can only be illustrative. Since the publication of the original BIOMASS methodology, consideration has been extended to include potentially exposed populations of biota (PEPs), in the context of dose assessment and protection of the environment. Considering the need for the development of transfer pathways from a source term to an end point (for either PEGs or PEPs), the exposure modes that may occur and those to be assessed quantitatively should be identified. Within an expert working group (WG6) of the second phase of the IAEA coordinated project Modelling and Data for Radiological Impact Assessments (MODARIA II), the experience of participating organisations has been collected on topics associated with the definition of PEGs and PEPs using a questionnaire. The objective of the questionnaire was to review the current status and on-going discussions on the handling of issues related to definitions of PEGs and PEPs as an input to the development of biosphere models for assessing radiological impacts on human health and the environment. The answers received to the questionnaire provided a clear overview of the progress that has been made since the original BIOMASS methodology was published, together with the lessons learned from the application of that methodology in the development of safety cases. This paper summarises the questionnaire responses in five subject areas: (1) environment of the PEGs and its evolution; (2) linking the choice of PEGs to these environments; (3) food habits and consumption rates; (4) populations of non-human biota (PEPs) and (5) national and international regulations and guidance. We illustrate how the results of the questionnaire have been used to enhance the original BIOMASS methodology (IAEA Enhanced BIOMASS Methodology Report in press).

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.227
GPT teacher head0.286
Teacher spread0.059 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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