Trail Smelter Déjà Vu: Extraterritoriality, International Environmental Law and the Search for Solutions to Canadian-U.S. Transboundary Water Pollution Disputes
Bibliographic record
Abstract
In the 1930s, a privately owned smelting plant in Trail, Canada was the focus of the most famous case in international environmental law: the Trail Smelter Arbitration. But the subject of that landmark case has not gone away. Over the last seventy years, the Trail smelter dumped millions of tons of mercury, arsenic, and toxic waste into the Columbia River. The dumping's effects have been felt in neighboring Washington State, where the toxic discharges have caused environmental harm. In 2003, the EPA began investigating the Washington border area for designation as a Superfund (CERCLA) site, and controversially demanded that the Trail smelter, which operates solely in Canada, submit to EPA jurisdiction and pay for cleanup costs. In July 2004, a Native American tribe filed a citizen's suit: the first time ever Americans have sued a Canadian company under the U.S. Superfund laws.\nThis article explores the United States's unprecedented attempt to apply its Superfund laws extraterritorially and to use domestic courts to resolve U.S.-Canadian transboundary water pollution disputes. In recent years, traditional barriers to relief in domestic courts have vanished. But using U.S. courts to solve international disputes is problematic for a variety of reasons. If transboundary disputes can not be solved diplomatically, the U.S. and Canada would be wise to resolve their transboundary pollution problems through international arbitration. This article analyzes the limitation of domestic law, and argues that the 1909 Boundary Waters Treaty and the landmark Trail Smelter Arbitration provides an appropriate framework to do so successfully.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.025 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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.
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".