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Record W3205384718 · doi:10.2172/1824967

Conduct weld campaign (FY-21-1) on irradiated materials provided by the Canadian Nuclear Laboratory (CNL), including baseline post-weld evaluation and testing

2021· report· en· W3205384718 on OpenAlexaffabout
Jian Chen, Zhili Feng, Roger G. Miller, S. White, Wenjing Li, Lori J. Walters, Jonathan Tatman, Greg Frederick

Bibliographic record

Venuenot available
Typereport
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsCanadian Nuclear Laboratories
FundersOak Ridge National LaboratoryUT-BattelleOffice of Nuclear EnergyBattelleDivision of Materials ResearchU.S. Department of Energy
KeywordsWeldingBaseline (sea)Nuclear engineeringIrradiationMaterials scienceRadiochemistryMetallurgyEngineeringForensic engineeringNuclear physicsPolitical scienceChemistryPhysicsLaw

Abstract

fetched live from OpenAlex

A collaborative research on developing advanced welding technologies for irradiated stainless steel 304 materials between CNL and ORNL has been established to support both U.S. and Canadian interests in evaluation of weld repair techniques on irradiated materials to support continuous operation of commercial nuclear power. The work utilizes unique material from the National Research Universal (NRU) reactor and the specialized welding hot cell facility at ORNL. The objective is to explore suitable welding technique and parameters and to determine the helium concentration limitation in terms of irradiated stainless-steel weldability. An additional objective is to develop the knowledge and understanding of cracking mechanisms induced by high concentration of helium in the process of weld repair of the irradiated structural alloys. This report summarizes the experimental welding evaluation on irradiated material from the NRU reactor containing helium concentrations ranging from 12 to 45 atomic parts per million (appm).

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.220
Threshold uncertainty score0.438

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.122
GPT teacher head0.314
Teacher spread0.193 · 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 designBench or experimental
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

Citations0
Published2021
Admission routes2
Has abstractyes

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Same topicNuclear Materials and PropertiesFrench-language works237,207