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Record W6940869866 · doi:10.11575/prism/26185

Flammability limits of hydrogen-diluent mixtures in air

2012· other· en· W6940869866 on OpenAlexfundno aff

Bibliographic record

VenuePRISM (University of Calgary) · 2012
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNucleofectionTSG101HyporeflexiaFusible alloyWindagePretext

Abstract

fetched live from OpenAlex

The flammability limits of hydrogen-diluent fuel mixtures in air for upward and downward flame propagation at ambient pressure were determined experimentally. A wide range of diluent concentrations were examined, up to 95% by volume in the fuel mixture. The diluents employed were helium, argon, carbon dioxide and nitrogen. The effects of changes of the initial mixture temperature on the flammability limits were determined for fuel mixtures containing 50% diluent by volume. It was observed that helium was more effective than argon at narrowing the flammable range of hydrogen mixtures. The value of the lean flammability limit was increased to a greater extent by the addition of helium than carbon dioxide despite the low heat capacity of helium. Values of the flammability limits predicted using the constant adiabatic flame temperature concept are in generally good agreement with the experimentally obtained values. The agreement between the two sets of values was better for the rich flammability limits than the corresponding lean flammability limits and was particularly poor for lean mixtures involving high concentrations of helium.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.182
Teacher spread0.172 · 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 designSimulation or modeling
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

Citations2
Published2012
Admission routes1
Has abstractyes

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