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Record W3215501155 · doi:10.1016/j.dib.2021.107633

Data related to architectural bone parameters and the relationship to Ti lattice design for powder bed fusion additive manufacturing

2021· article· en· W3215501155 on OpenAlex
Martine McGregor, Sagar Patel, Stewart McLachlin, Mihaela Vlasea

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueData in Brief · 2021
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaFedDev Ontario
KeywordsTitanium alloyLattice (music)FusionMaterials scienceAlloyLattice constantCrystal structureTitaniumCrystallographyComposite materialMetallurgyOpticsChemistryPhysics

Abstract

fetched live from OpenAlex

The data included in this article provides additional supporting information on our publication (McGregor et al. [1]) on the review of the natural lattice architecture in human bone and its implication towards titanium (Ti) lattice design for laser powder bed fusion and electron beam powder bed fusion. For this work, X-ray computed tomography was deployed to understand and visualize a Ti-6Al-4V lattice structure manufactured by laser powder bed fusion. This manuscript includes details about the manufacturing of the lattice structure using laser powder bed fusion and computed tomography methods used for analyzing the lattice structure. Additionally, a comprehensive literature review was conducted to understand how lattice parameters are controlled in additively manufactured Ti and Ti-alloy parts aimed at replacing or augmenting human bone. From this literature review, lattice design information was collected and is summarized in tabular form in this manuscript.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.001
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 head0.067
GPT teacher head0.283
Teacher spread0.216 · 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