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Record W4378903989 · doi:10.36253/techne-13656

Harnessing the natural intelligence of wood to improve passive ventilation in buildings

2023· article· en· W4378903989 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTECHNE - Journal of Technology for Architecture and Environment · 2023
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsUniversity of Waterloo
FundersEuropean Regional Development FundUniversity of WaterlooUniversità degli Studi di PerugiaRegione UmbriaUniversità degli Studi di Firenze
KeywordsArchitectural engineeringNatural ventilationNatural (archaeology)Environmental scienceNatural materialsRelation (database)Adaptation (eye)Computer scienceVentilation (architecture)EngineeringMaterials scienceMechanical engineering

Abstract

fetched live from OpenAlex

Wood actively equalises its moisture content in relation to its surrounding environment. Technical applications that can harness this characteristic can have a great impact in the improvement of indoor hygrometric comfort. So far few applications have made use of this unique property. The natural hygroscopic intelligence of wood can lead to the development of a new technology capable of ensuring improved indoor comfort. The natural material can thus be engineered by creating responsive composites made from wood waste and transformed through 4D printing. The biomimetic actuators studied in this paper are aimed at linking the transformation of form into environmental control functionality applied to building comfort in adaptive and passive solutions.

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 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 score0.714
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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