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Record W3088200492

МЕТОД ИЗВЛЕЧЕНИЯ ЭТАНА В СМЕСИ С МЕТАНОМ В ПРОМЫСЛОВЫХ УСЛОВИЯХ ПУТЕМ СОВМЕЩЕНИЯ ТРУБКИ РАНКА - ХИЛША И СОПЛА ЛАВАЛЯ

2017· article· ru· W3088200492 on OpenAlexaboutno aff
Р. Ф. Мияссаров, А. А. Ишмурзин, Р. А. Махмутов

Bibliographic record

VenueТерритория Нефтегаз · 2017
Typearticle
Languageru
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsRaw materialWaste managementHydrocarbonMethaneProcess engineeringEnvironmental scienceEngineeringChemistryOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

Ethane is the most efficient feedstock for ethylene, which is the main component for polyethylene production since no byproducts are produced from it. The research shows that ethane production increase is an energy consuming process. For instance, Orenburg helium factory annually consumes over 1 bln kWh of electricity. In this respect, special attention should be paid to the resource- and energy efficient processes in order to decrease operation temperatures and reduce metal content in the process equipment. The paper considers hydrocarbon separation method (methane and ethane) through combination of Ranque - Hilsch tube and Laval nozzle. The authors prove the necessity of hydrocarbon separation in field conditions.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.827
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0050.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.009

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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2017
Admission routes1
Has abstractyes

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