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Record W2890381673 · doi:10.5383/ijtee.16.02.004

Development of Measurement Techniques for Siloxanes in Landfill Gas

2018· article· en· W2890381673 on OpenAlexvenueno aff
Małgorzata Wzorek, Mirosława Kaszubska

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsLandfill gasEnvironmental scienceForensic engineeringWaste managementPetroleum engineeringEngineeringMunicipal solid waste

Abstract

fetched live from OpenAlex

In Poland, the biogas obtained from municipal solid waste landfills is most frequently used in biogas systems.In combustion of biogas contaminated with siloxanes, they transform and decompose to silica and silicate deposits, which affect the proper operation of power supply devices, i.e. electric generation systems or boilers.The study aimed to determine the optimal methods for the collection of representative biogas sample for gas chromatography (GC) analysis.The main tasks included the selection of the most favourable sampling conditions for siloxane sorption in a given sorbent, i.e. sampling time, gas flow speed and sorbent volume.The study regarded the landfill gas from the Municipal Landfill located in the city of Opole in Poland.Research showed the presence of organosilicon compounds in the tested biogas but their concentration was low.That's way the landfill gas can be successfully used for energy purposes without compromising the equipment caused by the presence of siloxanes

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.014
GPT teacher head0.215
Teacher spread0.201 · 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
Published2018
Admission routes1
Has abstractyes

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