MétaCan
Menu
Back to cohort
Record W2883624067 · doi:10.1097/hp.0000000000000892

Calibration of Radiation Portal Monitors for Characterization of Historic Low-level Radioactive Waste

2018· article· en· W2883624067 on OpenAlexafffundabout
David J. Cole, Nicolas Martin-Burtart

Bibliographic record

VenueHealth Physics · 2018
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsAmec Foster Wheeler (Canada)
FundersCanadian Nuclear Safety Commission
KeywordsTruckRadioactive wasteEnvironmental sciencePort (circuit theory)ScrapCalibrationRadiation monitoringSavannah River SiteWaste managementEngineeringNuclear medicine

Abstract

fetched live from OpenAlex

Radiation portal monitors are large-volume radiation detectors typically positioned along roadways, railways, and pedestrian portals. They are typically used for the detection of radioactive material where no such material is expected. Applications include monitoring vehicles and personnel exiting nuclear facilities, at the entrance to steel and scrap metal facilities, and at international borders. As part of the Port Hope Area Initiative, the Port Granby Project involves the relocation of approximately 450,000 m of historic low-level radioactive waste, located at an existing site on the shoreline of Lake Ontario in Southeast Clarington, to a new, engineered aboveground mound. Ongoing maintenance and monitoring will continue for hundreds of years after the facility is capped and closed. The waste is transported from its current location about 700 m to the new facility using trucks. Each truck passes through a radiation portal monitor when travelling between the waste location and waste storage facility (both directions; empty and full). The radiation portal monitors have been calibrated to provide an estimate of the total radioactivity being deposited into the new waste facility. This paper describes the methodology and results of the calibration.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.326

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 head0.023
GPT teacher head0.259
Teacher spread0.236 · 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 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
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueHealth PhysicsSame topicNuclear and radioactivity studiesFrench-language works237,207