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Record W4393583921 · doi:10.5281/zenodo.3820152

Multi-partner benchmark experiment of fatigue crack growth measurements from GDR 3651 FATACRACK

2020· dataset· en· W4393583921 on OpenAlexaff
Cusset Raphaël, Jérôme Hosdez, Bonnand Vincent, Benoît Blaysat, Menut-Tournadre Léa, Neggers Jan, Michel Coret, Henry Joseph, Doquet Véronique, Grédiac Michel, Vincent Chiaruttini, Martin Poncelet, Henry Proudhon, Limodin Nathalie, Julien Réthoré

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typedataset
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsBenchmark (surveying)Computer scienceGeographyCartography

Abstract

fetched live from OpenAlex

The dataset presents a synthesis of an experimental benchmark performed in the context of the French national research network GDR FATACRACK . A sample has been designed to produce mixed-mode crack propagation and variation of small scale yielding conditions. Two geometries and two maximum load levels are defined for the two tested materials: a stainless steel and an aluminum alloy. Around ten participants performed experiments using their usual instrumentation. Among the eight possible parameter sets, three are selected for which detailed results are presented. A satisfying overall agreement is obtained. But, some discrepancies are evidenced due either to limitations of the instrumentation or simply because from one lab to the other the applied load is not exactly the same. The results obtained by all the participants as well as the description of their testing setups and measurement tools are collected in this dataset. It includes crack path, stress intensity factor range and crack growth rate measurement for the selected configurations.

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)0.005
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0040.002
Research integrity0.0040.001
Insufficient payload (model declined to judge)0.0090.018

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 head0.089
GPT teacher head0.269
Teacher spread0.180 · 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
GenreDataset

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

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicFatigue and fracture mechanicsFrench-language works237,207