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Fundamental studies for designing insulation panels from wood shavings and filamentous fungi

2019· article· en· W2962727798 on OpenAlexafffund
Guido Wimmers, Julia Klick, Linda E. Tackaberry, Cora Zwiesigk, Keith N. Egger, Hugues B. Massicotte

Bibliographic record

VenueBioResources · 2019
Typearticle
Languageen
FieldMedicine
TopicFungal Biology and Applications
Canadian institutionsUniversity of Northern British Columbia
FundersLakehead University
KeywordsFungal growthEngineered woodThermal insulationTrametes versicolorSolid woodMaterials scienceEnvironmentally friendlyWood-plastic compositePulp and paper industryComposite materialEnvironmental scienceWaste managementComposite numberEngineeringBotanyLaccaseEcology

Abstract

fetched live from OpenAlex

The production of environmentally friendly thermal insulation boards is important for the building industry to reduce its environmental impact. The primary objective of this study was to test the feasibility of producing wood-based insulation panels as well as to use fungi as a binding agent and to explore whether a bio-based composite could be a viable alternative to the standard traditional foam insulation board and more expensive wood fibreboards (mainly available in European markets). Experiments were conducted to determine which combinations of wood fibers from selected northern tree species, wood decay fungi, and growth conditions were most suitable for panel making. The results showed that under the determined optimal growth conditions, Polyporus arcularius and Trametes suaveolens on birch wood shavings provided the best combination. Outcomes from initial physical screening tests, particularly thermal conductivity, suggested that these panels had a comparable performance to traditional insulation material.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score0.262

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)0.0000.000

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 head0.044
GPT teacher head0.309
Teacher spread0.265 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations37
Published2019
Admission routes2
Has abstractyes

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