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Record W1982248665 · doi:10.1115/imece2010-39376

Performance Comparison of Frame Structures: Discrete Michell or Wolff?

2010· article· en· W1982248665 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis and Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTensegrityTrussFrame (networking)Computer scienceSet (abstract data type)Orientation (vector space)Structural engineeringParallelsMathematical optimizationMathematicsEngineeringGeometryMechanical engineeringTelecommunications

Abstract

fetched live from OpenAlex

The presented work highlights parallels between natural and human designs for optimization purposes. Wolff’s law predicts that bone trabeculae orientation alterations within a dynamic environment occur in such a way as to use bone material in a structurally efficient manner; this occurs because trabeculae orientations align themselves along principal stress trajectories. Michell has also demonstrated how to optimize structures under a given a set of mechanical loads. Some researchers have recently defined a special case for optimal tensegrity structures that produces the discrete Michell truss. In this paper we have carried out a performance comparison between two different frame structures modeled based on the Wolff’s hypothesis and optimal tensegrity formulation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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