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Record W4247213061 · doi:10.46427/gold2020.31

New Developments in Diffusion Measurements Using Laboratory-Based X-Ray Sources

2020· article· en· W4247213061 on OpenAlexaff
Tom A. Al, Sam Morfin, Golrokh Hafezian, Charles Cadieux, Samuel K. Kumahor

Bibliographic record

VenueGoldschmidt Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTRACERAttenuationContext (archaeology)Materials sciencePorosityAbsorption (acoustics)SorptionDiffusionCharacterization (materials science)OpticsChemistryGeologyPhysicsNuclear physicsComposite materialNanotechnology

Abstract

fetched live from OpenAlex

Measurements of diffusion and sorption properties of rocks are key aspects of site characterization for a deep geological repository. Knowledge of diffusion properties is required for engineered barriers and for geologic materials in the near and far field surrounding a proposed geologic repository. Accordingly, diffusion investigations are commonly conducted at a variety of scales, ranging from 10s of mm to 100s of m. This work is focused on measurements conducted at the laboratory scale, most often using samples of drill core. In the context of radioactive waste management, X-ray radiography methods have been in use for decades The measurements have the advantage of being rapid and they provide time-and spatially-resolved tracer data. Studies commonly employ iodide as a tracer because it has a high Xray absorption cross section and it is transported conservatively in most geologic materials, but X-ray radiography has also been applied to study reactive transport of cesium Here we report on two new developments; i) X-ray absorption measurements using energy-dispersive X-ray spectrometers, and ii) a radiographic method suitable for low porosity rocks.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score1.000

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 head0.034
GPT teacher head0.233
Teacher spread0.200 · 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.

Study designSimulation or modeling
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
Published2020
Admission routes1
Has abstractyes

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