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Record W6980824076

Criteria for unacceptable damage on wood systems

2003· article· en· W6980824076 on OpenAlexvenueaboutno aff

Bibliographic record

VenueNPARC · 2003
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsLimitingWork (physics)DurabilityLeakage (economics)HumidityMoistureRelative humidity
DOInot available

Abstract

fetched live from OpenAlex

Advanced hygrothermal models can predict temperature and moisture conditions in wall components subjected to actual weather conditions, but damage functions are required to predict consequences for building performance. This paper documents progress towards criteria for unacceptable damage and discusses the work underway to define damage functions. Durability is addressed in Canadian building codes where it impacts safety or health. It, therefore, proposed that damage to building envelopes be considered in two ways. First, strength loss to the system, which determine its level of safety; second, effect on the health of the occupants from unacceptable levels of fungal spores or metabolites entering the living space. Damage functions for strength loss require an improved understanding of the limiting conditions of humidity and temperature for decay and of load distribution in platform-frame construction weakened by decay. Work is underway or planned in each of these areas and some preliminary results on time to establishment of decay in OSB will be presented. Damage functions for health impacts are very complicated to derive because it requires the knowledge of many interacting aspects. These aspects include knowledge of conditions affecting the type and amount of mould in the wall, air leakage or diffusion into the living space, exposure levels from exterior air, other building systems, health risk factors, and threshold levels for spores and metabolites to be determined by medical authorities. These threshold levels are also very difficult to establish because they vary from age group to another end even among individuals.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.746

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 head0.033
GPT teacher head0.309
Teacher spread0.276 · 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 designNot applicable
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

Citations4
Published2003
Admission routes2
Has abstractyes

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