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Record W3119305400

Izrada podzemnih skladišta prirodnog plina u solnim domama

2020· dissertation· sr· W3119305400 on OpenAlexaboutno aff
Monika Bakalović

Bibliographic record

Venuenot available
Typedissertation
Languagesr
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsNatural gasNatural gas storagePetroleum engineeringUnderground storageFossil fuelDrillingUnderground storage tankHomogeneousAquiferEnvironmental scienceWaste managementGeologyEngineeringGroundwaterGeotechnical engineeringStorage tankMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

Natural gas has been the cleanest-burning and fastest-growing fossil fuel in the last decade. Natural gas is stored in underground gas storage facilities in order to meet seasonal and peak demands. Salt caverns are the most common type of storage for covering peak loads because of the fast circulation of natural gas. The first gas storage in a salt cavern was constructed in 1959 in Canada. Underground storages of natural gas can be constructed in depleted gas/oil deposits, salt domes/salt rocks, and aquifers. Salt domes are homogeneous, which is why they are characterized by high stability and easy construction, i.e. leaching. The construction of an underground gas storage in salt caverns, among other things, includes the process of well drilling and well completion, the process of solution mining, monitoring the construction and operation of the cavern and the injection of natural gas into the newly created underground storage and monitoring the performance of natural gas storage.

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: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.003

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 head0.018
GPT teacher head0.280
Teacher spread0.262 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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Same topicReservoir Engineering and Simulation MethodsFrench-language works237,207