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Record W7746398 · doi:10.1212/wnl.45.5.865

On-Line Monitoring and Control of Radiochemical Streams at Spent Fuel Reprocessing Plant

2008· article· en· W7746398 on OpenAlexaboutno aff
Tatiana G. Levitskaia, Samuel A. Bryan

Bibliographic record

VenueOSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2008
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsWaste managementSpent nuclear fuelEnvironmental scienceRadioactive wasteInstrumentation (computer programming)Radiation monitoringNuclear fuelProcess engineeringRadiochemistryNuclear engineeringEngineeringChemistryComputer science

Abstract

fetched live from OpenAlex

Techniques are needed to provide on-line monitoring and control of the radiochemical processes that are being developed and demonstrated under the Global Nuclear Energy Partnership (GNEP) initiative. The instrumentation used to monitor these processes must be robust and must be able to withstand harsh radiation and chemical environments. A new on-line monitoring system satisfying these requirements featuring Raman spectroscopy combined with a Coriolis and conductivity probes, has been recently developed by our research team. It provides immediate chemical data and flow parameters of high-level radioactive waste streams with high brine/high alkalinity generated during retrieval from Hanford nuclear waste storage tanks. We are currently applying similar methodology for monitoring the radiochemical streams generated at the spent fuel reprocessing plant. The nature of these strems calls for additional spectroscopic information, which can be gained by the utilization of UV-vis-NIR capabilities.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.011
GPT teacher head0.215
Teacher spread0.203 · 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 designSimulation or modeling
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
Published2008
Admission routes1
Has abstractyes

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