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Record W3121298779

Divide and Conquer: Multiple Leasing in Common Pool Oil Fields

2002· article· en· W3121298779 on OpenAlexaff
Lasheng Yuan

Bibliographic record

VenueSSRN Electronic Journal · 2002
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDelegateLeaseProduction (economics)Land tenureBusinessOil productionMicroeconomicsIndustrial organizationEconomicsFinanceEngineeringComputer scienceGeographyPetroleum engineering
DOInot available

Abstract

fetched live from OpenAlex

A theoretic model is developed to analyse strategic leasing behaviours of U.S. landowners in a non-exclusively owned common oil pool. Oil field development is modelled as a simultaneous-move two-stage non-cooperative game. The landowners choose leasing strategies in the first stage; independent lease operators choose extraction strategies in the second. Ownership structure is distinguished from operation structure and their relationship analyzed. It is shown that, in a non-exclusively owned oil field, it is individually rational for a landowner to subdivide his landholding and delegate production rights to multiple independent firms, although more dispersed production control leads to heavier common pool losses.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.005
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.012
GPT teacher head0.249
Teacher spread0.237 · 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 designObservational
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
Published2002
Admission routes1
Has abstractyes

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