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Record W4403014420 · doi:10.1111/oet.13124

<scp>OPEC</scp> seeks to boost compliance amid demand uncertainty

2024· article· en· W4403014420 on OpenAlexaboutno aff

Bibliographic record

VenueOil and Energy Trends · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsCompliance (psychology)BusinessEconomicsPsychologySocial psychology

Abstract

fetched live from OpenAlex

OPEC seeks to boost compliance amid demand uncertaintySaudi Arabia wants to clamp down on OPEC-plus overproduction to improve credibility with the market amid uncertainty over demand.Pressure is growing on overproducers to comply with quotas and compensate for excess production, with catch up plans due by end-June.Crude over-production by the nine OPEC members subject to quotas rose by 100 000 b/d in May, leaving the group 320 000 b/d above its target ceiling.Output among OPEC members without quotas also rose slightly, although the non-OPEC part of OPEC-plus saw a fall of about 100 000 b/d. 1 The worst overproducers, Iraq, Russia, and Kazakhstan, have been given a deadline of the end of June to provide compensation plans.OPEC believes strict discipline is needed to bolster prices and avoid a global surplus in a context of rising output from non-members like United States, Canada and Guyana, and uncertainty over demand, especially in China (see Focus).The group has longstanding official reductions of 3.66 mn b/d, and on June 2, it agreed to extend production curbs into next year, although voluntary cuts made by eight top producers-currently around 2.2 mn b/d-will be only maintained at the same level for 3 months before being gradually eased up to September next year.2 How to cite this article: OPEC seeks to boost compliance amid demand uncertainty.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.108
Threshold uncertainty score0.360

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0060.002
Open science0.0020.003
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.1080.041

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 head0.032
GPT teacher head0.286
Teacher spread0.253 · 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 designNot applicable
Domainnot available
GenreOther

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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