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Record W2605247481 · doi:10.1016/j.dib.2017.03.043

Statistical data for the tensile properties of natural fibre composites

2017· article· en· W2605247481 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueData in Brief · 2017
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
FundersComposites Innovation Centre
KeywordsComposite materialMaterials scienceUltimate tensile strengthComposite numberEpoxyModulusStatistical analysisTensile testingYoung's modulusMathematics

Abstract

fetched live from OpenAlex

This article features a large statistical database on the tensile properties of natural fibre reinforced composite laminates. The data presented here corresponds to a comprehensive experimental testing program of several composite systems including: different material constituents (epoxy and vinyl ester resins; flax, jute and carbon fibres), different fibre configurations (short-fibre mats, unidirectional, and plain, twill and satin woven fabrics) and different fibre orientations (0°, 90°, and [0,90] angle plies). For each material, ~50 specimens were tested under uniaxial tensile loading. Here, we provide the complete set of stress-strain curves together with the statistical distributions of their calculated elastic modulus, strength and failure strain. The data is also provided as support material for the research article: "The mechanical properties of natural fibre composite laminates: A statistical study" [1].

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.171
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0050.003
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 head0.082
GPT teacher head0.322
Teacher spread0.240 · 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