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Record W2415536915 · doi:10.1088/0952-4746/36/2/s8

Interdisciplinary perspectives on dose limits in radioactive waste management. A research paper developed within the ENTRIA project

2016· article· en· W2415536915 on OpenAlexfundno aff
Karena Kalmbach, Klaus‐Jürgen Röhlig

Bibliographic record

VenueJournal of Radiological Protection · 2016
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsnot available
FundersInternational Atomic Energy AgencyBundesministerium für Bildung und ForschungNuclear Waste Management Organization
KeywordsInterdependencePoliticsWork (physics)Radioactive wasteRealmEngineering ethicsPolitical scienceSociologyRisk analysis (engineering)BusinessEngineeringLaw

Abstract

fetched live from OpenAlex

aiming at a synthesis of the technical, sociology of knowledge, legal, societal, and political aspects of dose limits within the field of radioactive waste management. In this paper, the ENTRIA project is briefly introduced and the work on dose limits is put into the perspective of this much larger project. Selected aspects of the ENTRIA work on dose limits related to the different roles such limits play for different actors as well as to the specific case of nuclear waste disposal are presented. The work recognizes that such limits are indispensable for technological developments and legal security but, at the same time, depend on country and project specific circumstances. This may result in serious conflicts and concerns in public debates as well as in the political realm. In order to better understand the interaction and interdependencies of these various contexts in which debates about dose limits play out, future interdisciplinary research is needed. This research should contribute to an open discourse on dose limits which reflects underlying values, objectives, actors and procedures that have defined present dose limit regimes. Additionally, this research should indicate paths for potential alternatives and complements to these established regimes.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.079
GPT teacher head0.334
Teacher spread0.255 · 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 designQualitative
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

Citations1
Published2016
Admission routes1
Has abstractyes

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