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Record W2763224533

Stochastic Environmental modeling for Nuclear Waste Management

2017· dissertation· en· W2763224533 on OpenAlex
Vimala Madhangi Madhusoothanan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUWSpace (University of Waterloo) · 2017
Typedissertation
Languageen
FieldMaterials Science
TopicGraphite, nuclear technology, radiation studies
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsEnvironmental scienceWaste managementEngineeringEnvironmental planning
DOInot available

Abstract

fetched live from OpenAlex

Deep geological repositories are identified as possible disposal site for safely isolating highly radioactive nuclear waste from affecting humans and the environment. These repositories are multi barrier systems and safety of the system is very crucial since failure of the system will lead to radioactive contamination, which is harmful to the environment. 
\n\tIt is necessary to model the possible failure of the system, one of the most significant parameter is the mass transfer between the barriers in the multiple barrier system given by equivalent flow rates, half time of the solute and the delay time between the inflow and outflow of the barriers. The entire model is constructed based on the conservation assumption of mass flux. The model is used to analyze radioactive decays of the two long lived radioactive species C-14 (neutral non-sorbing nuclide) and I-129 (anionic non-sorbing nuclide). From the radioactive decay of these radionuclides the equivalent exposure is calculated to ensure that it is well below the current safety limits specified by the Regulator. 
\n\tThe geosphere and bentonite buffer, which are a part of the multi barrier system, are porous media and modeling the seepage is done using Darcy’s law. Modeling seepage of water is important because water acts as a carrier for several elements that can potentially corrode the copper coating. The copper coating is an integral part of the multi barrier system, and an essential element of of the used fuel container.
\n This thesis analyzes effects of a wide spectrum of uncertainties on the performance of the analytical solution obtained from the deterministic model is used to (i) consider parameter uncertainties, and (ii) derive stochastic solution of governing equations for the following two cases: (1) water seepage into the DGR, and (2) Mass outflow of radioactive material. Case I a man-made system whose uncertain and time invariant parameters, whereas Case II considers stochastic nature of the natural environment. Conclusions from this study support a high level of safety aspects of DGR for the disposal of high level radioactive waste.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.200
Teacher spread0.188 · 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