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Record W2800486674 · doi:10.1520/jte20170645

Alternative Setup Apparatus to Test ASTM A944-10 Beam-End Specimens

2018· article· en· W2800486674 on OpenAlexaff
Pasquale Basso Trujillo, René Malo, Marc Jolin

Bibliographic record

VenueJournal of Testing and Evaluation · 2018
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceBeam (structure)Structural engineeringTest (biology)Composite materialForensic engineeringEngineeringGeology

Abstract

fetched live from OpenAlex

Abstract The setup apparatus described in ASTM A944-10, Standard Test Method for Comparing Bond Strength of Steel Reinforcing Bars to Concrete Using Beam-End Specimens, is designed to apply the load horizontally with respect to the laboratory floor. However, many civil engineering laboratories currently use (or have access to) rigid testing frames designed to apply the load vertically. Thus, a setup apparatus adapted to the current frames in use and alternate to the one described in ASTM A944-10 was developed and is thus presented. Following a brief review of the major characteristics of a typical ASTM A944-10 beam-end specimen, the proposed setup apparatus and the procedure to mount and test the specimen are described. Thereafter, the results of three groups of specimens having three replicas each are presented and are followed by a discussion on how to interpret and analyze the information obtained from the test.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.003

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 head0.062
GPT teacher head0.334
Teacher spread0.272 · 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 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

Citations4
Published2018
Admission routes1
Has abstractyes

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