Exploring New Shores: An Overview of the Legal Landscape Relating to the Supply and Transportation of LNG from Canadian Ports
Bibliographic record
Abstract
This article provides an overview of the key areas of the legal landscape downstream of liquefaction terminals in the context of Canadian LNG projects. This will be of particular interest to those involved in Canada's nascent LNG industry, including project proponents, buyers and sellers of LNG, and owners and operators of LNG carriers and other vessels intending to load LNG, either as cargo or bunker fuel, from a terminal in Canada. Canada’s LNG industry has had a rocky start in some respects, with many projects having been shelved and a number of the remaining projects facing an uncertain future due to market conditions, regulatory obstacles, and other challenges. However, there continue to be many factors supporting the development of LNG liquefaction capacity in Canada, including the desire to supply the world with responsibly sourced, cost-effective, and relatively clean fuel, and it is expected that a number of projects will ultimately proceed. The supply and transportation of LNG from such projects once they become operational will engage the body of maritime laws governing the operation of ships in Canadian waters and, where relevant, Canada’s export laws. Other requirements may additionally apply by virtue of the contractual arrangements typically entered into in connection with such activities. Such regulatory and other requirements, and their place within the broader LNG value chain, are the subject of this article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".