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Record W2063350767 · doi:10.1520/jte100980

Improvements in the Mercury Displacement Method for Measuring Diffusible Hydrogen Contents in Steels

2009· article· en· W2063350767 on OpenAlexaff
Thushanthi D. A. A. Senadheera, William J. Shaw

Bibliographic record

VenueJournal of Testing and Evaluation · 2009
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMercury (programming language)HydrogenMetallurgyMaterials scienceDisplacement (psychology)Forensic engineeringComposite materialChemistryEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract The mercury displacement method, also known as the eudiometer method, is one of the most widely used, reliable, and low cost techniques of determining hydrogen quantities in steels. This is one of the few direct and simple techniques for measuring diffusible (mobile) hydrogen quantities. It was originally developed for measuring hydrogen pickup in steels as a result of the welding process, ANSI/AWS A 4.3 standard. However, it is often applied in a more general sense to the measurement of mobile hydrogen in steels as picked up from a wide variety of sources. The main limitation in using this technique is one of safety concerns in handling mercury and controlling the mercury fumes that are given off. This paper describes and suggests a number of modifications and improvements that have been developed from experience over the past couple of years. The modifications increase the accuracy and significantly enhanced the level of safety. The system can be run at moderately high temperatures up to 180°C with minimal mercury vapor loss.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.887
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 head0.102
GPT teacher head0.324
Teacher spread0.222 · 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 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

Citations2
Published2009
Admission routes1
Has abstractyes

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