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Record W6928874467 · doi:10.4224/20374371

An Experimental Study of the Effect of Thermal Radiation Feedback on the Room-Burning Behaviour of Horizontal Slabs of Polyurethane Foam

2011· report· en· W6928874467 on OpenAlexvenueno aff

Bibliographic record

VenueNPARC · 2011
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCalorimeter (particle physics)ThermalGlass woolThermal radiationPolyurethaneRodRadiationThermal insulationInstrumentation (computer programming)

Abstract

fetched live from OpenAlex

This report presents the results of three fire experiments (one free-burn and two room tests) that were carried out to investigate the influence of thermal radiation feedback on the rate of surface flame spread and heat release rate (HRR) for a horizontal block of furniture-grade non-fire-retarded polyurethane foam measuring 1200 x 600 x 200 mm and weighing approximately 4.8 kg. The room tests were conducted in a small compartment measuring 2400 mm wide x 2800 mm deep x 2400 mm high with a rectangular vent (opening under a calorimeter hood) measuring 740 mm wide x 1500 mm high (a ventilation limit of approximately 2000 kW) located in one of the 2400 mm walls. The room was lined with one of two different non-combustible materials - 12.7 mm thick cement board or 50 mm thick mineral wool insulation - with substantially differential thermal inertias in order to subject the test specimen to one of two thermal environments. Measurements were taken to quantify the temporal variation of heat release rates (HRRs), smoke density, radiant heat flux, temperatures and the concentration of O2, CO2 and CO in the test room. The tests were also recorded using an infrared camera in order to determine the surface rate of flame spread.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.304
Teacher spread0.275 · 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 designBench or experimental
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

Citations1
Published2011
Admission routes1
Has abstractyes

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