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Record W2303198497 · doi:10.12943/cnr.2015.00045

APPLICATION OF THE ADVANCED ATMOSPHERIC PLUME PROFILER TO CURRENT CHALK RIVER LABORATORIES MONITORING SYSTEMS

2015· article· en· W2303198497 on OpenAlexaffvenueabout
Antoine Boyer, Matthew Border, A.L.M. Ethier, Paul Leeson

Bibliographic record

VenueAECL Nuclear Review · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsEnvironmental sciencePlumeAtmospheric dispersion modelingCurrent (fluid)MeteorologySampling (signal processing)Remote sensingAir pollutionGeologyOceanographyTelecommunicationsEngineeringGeography

Abstract

fetched live from OpenAlex

The Advanced Atmospheric Plume Profiler (AAPP) was used to model emissions from facilities at Canadian Nuclear Laboratories (CNL, formerly Atomic Energy of Canada Limited). The model results were found to compare well with results from the current atmospheric monitoring program at the Chalk River Laboratories (CRL). The AAPP is a dispersion model designed and developed by CNL to model multiple emission sources from CRL operations. The AAPP used in conjunction with in-situ sampling can also estimate emissions from sources that are difficult to access or directly measure.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.368
Threshold uncertainty score0.731

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.239
Teacher spread0.227 · 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 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

Citations0
Published2015
Admission routes3
Has abstractyes

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