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Record W4404703115 · doi:10.5194/adgeo-65-55-2024

Lessons from national approaches: a long uphill struggle in search of sites for repositories for nuclear waste locations

2024· article· en· W4404703115 on OpenAlexaboutno aff
Thomas Flüeler

Bibliographic record

VenueAdvances in geosciences · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsnot available
Fundersnot available
KeywordsNuclear powerRadioactive wasteQuarter (Canadian coin)ReputationSpent nuclear fuelBusinessSilver bulletVisibilityPolitical scienceEnvironmental planningPublic administrationOperations managementLawEnvironmental scienceEngineeringHistoryWaste managementGeographyArchaeologySociologyMeteorology

Abstract

fetched live from OpenAlex

Abstract. A quarter of a century ago, a long-term expert in the nuclear waste scene stated that “the management of spent nuclear fuel and high-level nuclear waste has the deserved reputation as one of the most intractable policy issues facing the United States and other nations using nuclear reactors for electric power generation” (North, 1999, p. 751). Apart of exceptions, this statement is still true. At some points, however, there is light at the end of the tunnel if we can read the signs of the times. It will be a long hike, in steep terrain, poor visibility and with an approximate destination. We need a safe and acceptable site, tolerated by the affected parties, where a repository can be built, operated and, finally, closed down in reasonable course by a generation to come and with a clear conscience. This contribution does not present the silver bullet (which does not exist) but suggests some criteria and characteristics which have not been respected in the history of final disposal – but they should be. It needs adequate resources: stable structures, competent institutions, learning personnel (in institutions and civil society), mature and open discourse as well as sufficient time. Based on https://doi.org/10.5194/egusphere-egu24-6514 (Flüeler, 2024a).

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.083
GPT teacher head0.394
Teacher spread0.311 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes1
Has abstractyes

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