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Record W1992713250 · doi:10.3200/aeoh.58.8.523-527

Creating Mold-Free Buildings: A Key to Avoiding Health Effects of Indoor Molds

2003· article· en· W1992713250 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.

Bibliographic record

VenueArchives of Environmental Health An International Journal · 2003
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsHealth Research Foundation
Fundersnot available
KeywordsApartmentMoldKey (lock)Architectural engineeringSick building syndromeBusinessOperations managementComputer scienceEngineeringIndoor air qualityCivil engineeringComputer security

Abstract

fetched live from OpenAlex

In view of the high costs of building diagnostics and repair subsequent to water damage--as well as the large medical diagnostic and healthcare costs associated with mold growth in buildings--commitment to a philosophy of proactive preventive maintenance for home, apartment, school, and commercial buildings could result in considerable cost savings and avoidance of major health problems among building occupants. The author identifies common causes of mold growth in buildings and summarizes key building design and construction principles essential for preventing mold contamination indoors. Physicians and healthcare workers must be made aware of conditions within buildings that can give rise to mold growth, and of resulting health problems. Timely advice provided to patients already sensitized by exposure to molds could save these individuals, and their families, from further exposures as a result of inadequate building maintenance or an inappropriate choice of replacement housing.

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 score0.597
Threshold uncertainty score0.732

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 head0.006
GPT teacher head0.261
Teacher spread0.255 · 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