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Biodeterioration Models for Building Materials: Critical Review

2019· article· en· W2980094374 on OpenAlexaff
Robert Lepage, Samuel V. Glass, W. H. C. Knowles, Phalguni Mukhopadhyaya

Bibliographic record

VenueJournal of Architectural Engineering · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicBuilding materials and conservation
Canadian institutionsD-Wave Systems (Canada)University of Victoria
Fundersnot available
KeywordsArchitectural engineeringEnclosureSustainabilityBuilding designEngineeringBuilding scienceBuilt environmentConstruction engineeringField (mathematics)Civil engineeringEcology

Abstract

fetched live from OpenAlex

Biodeterioration of building materials due to poor hygrothermal conditions is a major concern for the sustainability of buildings and the health and safety of occupants. The risks of biodeterioration are accentuated in high-efficiency buildings, requiring further design considerations. Researchers across the world have tried to characterize this issue through a combination of field experience, modeling, and controlled laboratory investigations. However, integration of these research outputs in building enclosure design analysis is an unfinished agenda, partly due to the lack of coordination between engineering researchers, building enclosure designers, and biologists. This paper critically reviews the research to date on biodeterioration models of building materials (e.g., wood) from the perspective of a building scientist and identifies the needs for further research initiatives that will facilitate the integration of biodeterioration models in building enclosure design analysis through national and international building code regulations and standards.

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)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.004
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0040.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.003

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 head0.020
GPT teacher head0.233
Teacher spread0.212 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations24
Published2019
Admission routes1
Has abstractyes

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