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Foreign Experience of Applying the Principle of "Pump or Pay" in the Field of Pipeline Transportation

2015· article· en· W2520304563 on OpenAlexaboutno aff
Valery I. Salygin, Igbal A. Guliyev, Alisa O. Khubaeva

Bibliographic record

VenueMGIMO Review of International Relations · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsObligationContext (archaeology)BusinessPipeline (software)Pipeline transportCapital (architecture)EconomicsFinanceEngineeringLaw

Abstract

fetched live from OpenAlex

This article reveals the practice of "ship or pay" principle in the US, Canada and Europe. The authors analyze the practice of concluding contracts for oil and petroleum products transportation, procedures, terms and conditions stipulated in the contract. The "take or pay" principle is a common practice in developed countries like the US, Canada and the UK. The specific feature of the United States is that the pipelines are not built only for one shipper, but rather for all market, which is caused the "open season" tradition. In Canada, "take or pay" principle applies to cover the capital costs of the carrier. The main reasons for usage of terms "take or pay" are to minimize risks of the carrier, building or expanding his own pipeline network, by guaranteeing shipper's financial benefits after the putting pipeline into operation. "Take or pay" contracts cover the carrier's obligation to provide agreed minimum amount of petroleum to the consignor within a certain period. In turn, the shipper is obliged to accept the minimum amount of petroleum and pay, regardless of the fact of acceptance of oil. "Take or pay" principle is a kind of risk-sharing mechanism, which allows to shift the risks of non-fulfillment of the contract to the shipper. Besides, the "take or pay" principle can be indirect guarantee in the context of project financing, and therefore, financing. The article emphasizes the main advantages of the application of this principle and opportunities for its use in Russia.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.858
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.067
GPT teacher head0.420
Teacher spread0.353 · 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 designTheoretical or conceptual
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
Published2015
Admission routes1
Has abstractyes

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