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Record W2184656522

The Low-Level Radioactive Waste Management Office: Thirty Years of Experience in Canada - 13308

2013· article· en· W2184656522 on OpenAlexaboutno aff
Liliana Benitez, Mark J. Gardiner, Robert L. Zelmer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsnot available
Fundersnot available
KeywordsRadioactive wasteGovernment (linguistics)BusinessEnvironmental planningEngineeringWaste managementEnvironmental science
DOInot available

Abstract

fetched live from OpenAlex

This paper reviews thirty years of progress by the Low-Level Radioactive Waste Management Office (LLRWMO) in developing and implementing low-level radioactive waste (LLRW) remediation projects and environmentally safe co-existence strategies. It reports on the present status and the future of the national historic waste program in Canada. There are over two million cubic metres of historic LLRW in Canada. Historic LLRW is broadly defined as LLRW that was managed in the past in a manner that is no longer considered acceptable and for which the original owner cannot reasonably be held accountable. In many cases, the original owner can not be identified or no longer exists. The LLRWMO was established in 1982 as Canada's agent to carry out the responsibilities of the federal government for the management of historic LLRW. The LLRWMO is operated by Atomic Energy of Canada Limited (AECL) through a cost-recovery agreement with Natural Resources Canada (NRCan), the federal department that provides the funding and establishes national policy for radioactive waste management in Canada. The LLRWMO expertise includes project managers, environmental remediation specialists, radiation surveyors, communications staff and administrative support staff. The LLRWMO in providing all aspects of project oversight and implementation contracts additional resources supplementing coremore » staff capacity as project/program demands require. (authors)« less

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.637

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.194
Teacher spread0.183 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2013
Admission routes1
Has abstractyes

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Same topicNuclear and radioactivity studiesFrench-language works237,207