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Record W2344731973 · doi:10.1177/0731684415627296

Hybrid bonded-fastened joints and their application in composite structures: A general review

2016· review· en· W2344731973 on OpenAlexaff
Kobyé Bodjona, Larry Lessard

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2016
Typereview
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceAdhesive bondingAdhesiveComposite numberComposite materialStructural engineeringFocus (optics)Forensic engineeringMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Hybrid bonding-fastening is an alternative joining technique that consists of simultaneous adhesive bonding and mechanical fastening of the adherends. In this paper, pertinent scientific publications are selected and reviewed in an attempt to synthesize the current knowledge on various aspects of this technique. A particular focus is maintained on its application in composite structures. Two major bonding-fastening methods, namely bonding-bolting and bonding-pinning, are identified and contrasted, and the characteristics of each are discussed. Existing gaps in the literature are identified. Finally, perspectives for future research are assessed.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.014
GPT teacher head0.250
Teacher spread0.237 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations97
Published2016
Admission routes1
Has abstractyes

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