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Summary of the 1st International Workshop on Environmental, Safety and Economic Aspects of Fusion Power

2016· article· en· W2511403607 on OpenAlexaff
Yican Wu, Eef Stevens, K. Kim, D. Maisonnier, A.G. Kalashnikov, K. Tobita, David Jackson, C. Alejaldre, Didier Perrault, D. Panayotov, B. Merrill, C. Grisolia, Massimo Zucchetti, T. Pinna, D. van Houtte, Satoshi Konishi, B.N. Kolbasov

Bibliographic record

VenueNuclear Fusion · 2016
Typearticle
Languageen
FieldMaterials Science
TopicFusion materials and technologies
Canadian institutionsMcMaster University
FundersNational Magnetic Confinement Fusion Program of China
KeywordsAgency (philosophy)Presentation (obstetrics)Fusion powerNuclear powerAtomic energyPolitical scienceBusinessAeronauticsEngineeringMedicineSociologyPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Abstract The 1st International workshop on Environmental, Safety and Economic Aspects of Fusion Power (ESEFP) was held on 13 September 2015 at Jeju Island, South Korea. The workshop was initiated by the International Energy Agency Implementing Agreement on a Co-operative Program on ESEFP. The workshop was well attended with about forty participants representing twelve institutions in ten countries. The presentations covered safety issues and environmental impacts, availability improvement and risk control and socio-economic aspects of fusion power. Safety and licensing gaps between DEMO and ITER were discussed in depth with the consensus output presented as a plenary presentation at the 12th International Symposium on Fusion Nuclear Technology (ISFNT-12). The next workshop is planned to be held in conjunction with the ISFNT-13 in 2017.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0020.000
Scholarly communication0.0050.003
Open science0.0020.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0700.028

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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations14
Published2016
Admission routes1
Has abstractyes

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