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Record W2480048637

Some Recurring Issues in Operating Agreements and What AAPL's Drafting Committee Might Do About Them

2014· article· en· W2480048637 on OpenAlexaboutno aff
John S. Lowe

Bibliographic record

VenueSMU Scholar (Southern Methodist University) · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science
DOInot available

Abstract

fetched live from OpenAlex

The inherent inefficiency of reinventing the wheel for every drilling venture led to the development of the first American Association of Professional Landmen (AAPL) Form 610 Model Form Operating Agreement in 1956 (AAPL Form 610-1956). Revised forms followed in 1977, 1982, and 1989. The AAPL model forms have become the standard in the United States, and will be the focus of this chapter. The AAPL has begun the process of revising the AAPL Form 610-1989 Model Form Operating Agreement (AAPL Form 610-1989),19 and over the next couple of years everyone active in the industry likely will find themselves discussing what the drafting committee proposes or what the drafting committee ought to do. This chapter will consider some issues that commonly arise with operating agreements and suggest to the drafting committee some changes that they might consider. In particular, this chapter will focus on what we can learn from others, looking at some of the provisions in the Association of International Petroleum Negotiators (AIPN) 2012 Model International JOA (AIPN 2012 JOA), the Canadian Association of Petroleum Landmen (CAPL) 2007 Operating Procedure (CAPL 2007 Operating Procedure), the Australian Mining and Petroleum Law Association (AMPLA) Model Petroleum Joint Operating Agreement (AMPLA 2011 JOA), the United Kingdom Offshore Operators Association (UKOOA) Model Form JOA (UKOOA 2009 JOA), and the model forms issued by the Rocky Mountain Mineral Law Foundation. This chapter will also examine the more recent AAPL drafting ventures for offshore operations, which the AAPL drafting committee might consider to address the recurring issues that will be discussed. Due to time and space limitations, the focus will be on issues related to operators — particularly operator liability and operator removal.

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)0.151
metaresearch head score (Gemma)0.267
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.151
Threshold uncertainty score0.798

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1510.267
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0040.011
Science and technology studies0.0280.039
Scholarly communication0.0450.043
Open science0.0100.009
Research integrity0.0300.046
Insufficient payload (model declined to judge)0.0060.002

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 head0.020
GPT teacher head0.236
Teacher spread0.215 · 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
GenreCommentary

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
Published2014
Admission routes1
Has abstractyes

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