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Record W4390582530 · doi:10.1051/e3sconf/202447103002

Improving environmental efficiency of gas purification by low-temperature treatment

2024· article· en· W4390582530 on OpenAlexaboutno aff
Vladimir Babin, Maxim Yu. Romanenko, Yuri E. Serebrennikov, Vladimir V. Chernetsov, Gurban H. Dadashov

Bibliographic record

VenueE3S Web of Conferences · 2024
Typearticle
Languageen
FieldEngineering
TopicEngineering and Environmental Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleInletIdeal gasEnvironmental scienceGas pressureOverall pressure ratioMechanicsMaterials scienceMechanical engineeringPetroleum engineeringNuclear engineeringThermodynamicsEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper discusses low-temperature gas purification to improve environmental efficiency. Modern methods of gas cooling are also considered. A mathematical model is proposed for changing the parameters of an ideal gas when flowing through an opening - a Laval nozzle. A graph of the temperature of the gas at the outlet of the Laval nozzle is plotted at a different pressure ratio at the inlet and outlet of the nozzle. The conclusion was made based on the obtained values.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.406

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 head0.004
GPT teacher head0.171
Teacher spread0.167 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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