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Record W3141333796

The Vacuum in Nigeria's Crude Oil Laws: An Inquiry into the Decommissioning of Onshore and Offshore Facilities

2018· article· en· W3141333796 on OpenAlexaboutno aff
Kato Gogo Kingston, Z. Adangor

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNuclear decommissioningAbandonment (legal)LegislationCrude oilBusinessFossil fuelQuarter (Canadian coin)Submarine pipelinePetroleum engineeringLawEnvironmental planningNatural resource economicsEngineeringWaste managementPolitical scienceEnvironmental scienceEconomicsGeographyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Conventional wisdom requires that at the completion of crude oil production, the oil wells should be permanently plugged to protect the environment. Although, crude oil production has been undertaken for decades in Nigeria, there is no evidence to show that any facility has been properly decommissioned. In the United States, about three-quarter of the 50 states have plugging rules governing the procedures for plugging and abandonment of oil and gas wells. There is currently no official legal and institutional arrangement for proper decommissioning of oil and gas facilities in Nigeria. It is this loophole that this article seeks to investigate with regards to the effectiveness of Nigeria’s legal and institutional policies on abandonment and decommissioning of oil and gas facilities. It suggests that, there is currently a vacuum in the laws and that, the enactment of adequate legislation is urgently required.

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)0.007
metaresearch head score (Gemma)0.018
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.011
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.235
Teacher spread0.227 · 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
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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