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Record W3048075362

HYBRID SYSTEMS USING SMRS: A PATH TOWARDS SUSTAINABILITY IN NUCLEAR AND CANADIAN ENERGY PRODUCTION

2020· dissertation· en· W3048075362 on OpenAlexaboutno aff
Stephanie Bysice

Bibliographic record

VenueMacSphere (McMaster University) · 2020
Typedissertation
Languageen
FieldChemical Engineering
TopicMolten salt chemistry and electrochemical processes
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityProduction (economics)Energy (signal processing)Path (computing)Environmental economicsNatural resource economicsComputer scienceEconomicsPhysicsEcologyBiologyMicroeconomics
DOInot available

Abstract

fetched live from OpenAlex

Nuclear technology development in Canada has been relatively stagnant since the 1980s, when CANDU reactors were first implemented into the power grid. Reprocessing technologies such as pyroprocessing and the fluoride volatility method would introduce new opportunities for numerous industries throughout Canada. Transmutation of minor actinides and fission products have been proven to ease requirements of fuel repositories due to the reduction in radioactivity. Economic advantages from implementing SMRs in various industrial systems, including Canadian oil sands, would increase efficiency while decreasing CO2 emissions.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.400
Threshold uncertainty score0.805

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.007
GPT teacher head0.184
Teacher spread0.177 · 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
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
Published2020
Admission routes1
Has abstractyes

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