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Record W2613288907 · doi:10.29173/alr478

Recent Developments in Surface Rights Law - Pipeline Right-of-Way Compensation - Annual Payments and Injurious Affection - Federal and Alberta Developments

2005· article· en· W2613288907 on OpenAlexvenueaboutno aff
Lars Olthafer

Bibliographic record

VenueAlberta Law Review · 2005
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsCompensation (psychology)AppealPaymentLawBusinessHigh CourtPolitical scienceFinance

Abstract

fetched live from OpenAlex

Landowners have recently advanced novel claims for right-of-way compensation in connection with both federally and provincially regulated pipelines. The compensation paid for the acquisition of pipeline rights-of-way in Canada has typically been, with some notable exceptions, in the form of one-time payments for the value of the interest in land acquired, and any injurious affection to the remaining lands of the owner. However, relying on the precedents established by a few pipeline companies and the provisions for annual or periodic compensation under the Alberta Surface Rights Act and the National Energy Board Act landowners have attempted to secure compensation awards in the form of rental payments, the present value of which is several times greater than the compensation historically payable as a lump sum. This article examines two recent court decisions — the Alberta Court of Queen's Bench decision in Zubick v. Corridor Pipeline Limited and the Federal Court of Appeal's ruling in Balisky v. Canada (Minister of Natural Resources) — as well as a group of National Energy Board Act Pipeline Arbitration Committee awards in Alberta, and discusses their impact on pipeline rights-of-way compensation.

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)0.004
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: Review · Consensus signal: none
Teacher disagreement score0.077
Threshold uncertainty score0.560

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0070.011
Scholarly communication0.0110.002
Open science0.0020.002
Research integrity0.0060.003
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 head0.006
GPT teacher head0.228
Teacher spread0.223 · 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
GenreReview

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
Published2005
Admission routes2
Has abstractyes

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