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Low intensity gamma-ray monitor for in-situ solids fraction measurements in liquids

2022· article· en· W4309896661 on OpenAlexaff
Talwinder Kaur Sraw, Bo Yu, Andrea Sedgwick, Abu Junaid, Ying Y. Tsui, Manisha Gupta, R. Fedosejevs

Bibliographic record

VenueJournal of Instrumentation · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsCanadian Natural ResourcesNorthern Alberta Institute of TechnologyUniversity of Alberta
Fundersnot available
KeywordsScintillatorDetectorMaterials scienceTailingsOrthosilicateGamma rayAttenuationScintillation counterEnvironmental scienceOpticsPhysicsNuclear physicsTetraethyl orthosilicate

Abstract

fetched live from OpenAlex

Abstract A novel detection system has been designed to measure the settling of solids in tailing ponds in order to facilitate the reuse of water in oil sands extraction processing, tailings excavation, transportation and management. The system is based on a weak gamma ray source and an inexpensive scintillator-based detector. The system measures gamma ray photons which are transmitted through the material of interest which are detected by a combined scintillator and Multi Pixel Photon Counter (MPPC) detector. This non-destructive measurement allows the determination of the solids contents profile versus depth in a tailing pond. The attenuation of gamma radiation depends on the density of the solids within the fluid tailings and thus varies with the weight fraction of solids content of the tailings. Modelling of the system was carried out by Geant4 simulations. The system was deployed using a weak 133 Ba gamma-ray source together with a simple microprocessor controller readout circuit to analyze the pulse height response of a Cerium doped Lutetium Yttrium Orthosilicate (LYSO (Ce)) scintillator crystal. The detection system is calibrated with the known samples and can measure solids content with a relative precision of within ∼2%.

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 score0.382
Threshold uncertainty score0.278

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 head0.025
GPT teacher head0.281
Teacher spread0.256 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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