MétaCan
Menu
← Back to cohort
Record W7056556182

Fallow Fields Initiatives and Canada's East Coast Offshore:\nPolicy and Legal Considerations

2007· article· en· W7056556182 on OpenAlexaffabout

Bibliographic record

VenueeYLS (Yale Law School) · 2007
Typearticle
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsCanada Energy Regulator
Fundersnot available
KeywordsJurisdictionStatutory lawEast coastLegislatureGovernment (linguistics)State (computer science)Submarine pipelineLegislation
DOInot available

Abstract

fetched live from OpenAlex

The author examines various approaches adopted by government to balance the state's interest in promoting the timely and efficient exploration and development of oil and gas resources under state jurisdiction and industry's need for legal regimes providingsecurityoftenure and other conditions necessary for commercial success. In particular, the paper considers fallow field initiatives adopted by the United Kingdom in respect of the North Sea and their possible application to government's management of oil and gas resources in the Canadian east coast offshore areas, addressing applicable policy considerations, the legislative history of the statutory frameworks in place, and relatedjurisprudence.

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)0.002
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.068
Threshold uncertainty score0.493

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0200.010
Scholarly communication0.0080.002
Open science0.0020.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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

Citations0
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueeYLS (Yale Law School)→Same topicMagnetic Field Sensors Techniques→French-language works237,207→