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Record W4288048281 · doi:10.20898/j.iass.2022.012

The Design And Fabrication Of Mycocreate 2.0: A Spatial Structure Built With Load-Bearing Mycelium-Based Composite Components

2022· article· en· W4288048281 on OpenAlexaff
Ali Ghazvinian, Arman Khalilbeigi, Esmaeil Mottaghi, Benay Gürsoy

Bibliographic record

VenueJournal of the International Association for Shell and Spatial Structures · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and Biological Electrophysiology Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMyceliumLoad bearingFabricationMaterials scienceComposite materialBotanyBiology

Abstract

fetched live from OpenAlex

MycoCreate 2.0 is a spatial structure with load-bearing components made of mycelium-based composites, built for the 2022 Biomaterials Building Exposition at the University of Virginia, and has been initially conceptualized for the 2021 IASS Innovative Lightweight Structures Competition. Mycelium-based composites are lightweight, renewable, and biodegradable biomaterials obtained from mycelium, the root systems of fungi. There is a growing interest in mycelium-based materials from the architecture community, mainly due to their sustainable features. With MycoCreate 2.0, we employed a computational form-finding strategy for funicular, component-based structures fabricated with mycelium-based materials and an affordable and sustainable fabrication strategy to minimize waste. In addition, we tapped into the structural aspects of mycelium-based composites, their lightness, and biodegradability while easing the breathing and compaction of the material within the formworks.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.012
GPT teacher head0.200
Teacher spread0.188 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations9
Published2022
Admission routes1
Has abstractyes

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