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Record W2588676897 · doi:10.1093/rpd/ncw353

Information Management System Supporting a Multiple Property Survey Program with Legacy Radioactive Contamination

2017· article· en· W2588676897 on OpenAlexaffabout
R.H. Stager, Douglas B. Chambers, Gerd Wiatzka, Monica Dupre, Micah Callough, John Benson, Erwin Santiago, Walter van Veen

Bibliographic record

VenueRadiation Protection Dosimetry · 2017
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsCanadian Nuclear LaboratoriesArcadis (Canada)
Fundersnot available
KeywordsRadioactive contaminationContaminationProperty (philosophy)Radioactive wasteEnvironmental scienceComputer scienceWaste managementEngineering

Abstract

fetched live from OpenAlex

The Port Hope Area Initiative is a project mandated and funded by the Government of Canada to remediate properties with legacy low-level radioactive waste contamination in the Town of Port Hope, Ontario. The management and use of large amounts of data from surveys of some 4800 properties is a significant task critical to the success of the project. A large amount of information is generated through the surveys, including scheduling individual field visits to the properties, capture of field data laboratory sample tracking, QA/QC, property report generation and project management reporting. Web-mapping tools were used to track and display temporal progress of various tasks and facilitated consideration of spatial associations of contamination levels. The IM system facilitated the management and integrity of the large amounts of information collected, evaluation of spatial associations, automated report reproduction and consistent application and traceable execution for this project.x.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0270.014

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 head0.234
Teacher spread0.221 · 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 designNot applicable
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
Published2017
Admission routes2
Has abstractyes

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