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Record W4408627163 · doi:10.5006/mp2020_59_8-52

Moisture Drainage and Stand-Offs Impact on Insulation Wetting

2020· article· en· W4408627163 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

VenueMaterials performance · 2020
Typearticle
Languageen
FieldChemistry
TopicAerogels and thermal insulation
Canadian institutionsEmissions Reduction Alberta
Fundersnot available
KeywordsMoistureWettingDrainageEnvironmental scienceMaterials scienceGeotechnical engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

Corrosion under insulation (CUI) can cause prevalent degradation for plant piping and requires measures for detection and mitigation. Wet insulations are drivers for CUI, so minimizing moisture intrusion, or timely removal if intruded, can minimize CUI. In this study, insulated pipe assemblies with stand-offs and low-point drains were studied for drainage performance. Insulated pipes were soaked with measured aliquots of water followed by quantification of drained water. Patterns of moisture trapping underneath insulation were visualized using moisture detection imaging. A contact-free insulation system with low-point drains achieved maximum drainage efficiency (97%) and the least moisture trapping.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.701

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 head0.017
GPT teacher head0.240
Teacher spread0.223 · 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