The Responsibility of Transboundary Haze Pollution: The Case of Wildfire in Canada
Bibliographic record
Abstract
Responsibility for losses other countries suffer due to haze pollution is a serious issue. Transboundary haze pollution responsibility is related to the impact of smog pollution in one country and negatively impacts other countries in the vicinity. A country should take responsibility for forest fires out of respect for the country and its citizens. One example is the forest fires in Canada that spread smoke to neighbouring countries. This involves cooperation between countries to reduce the risk of transboundary haze pollution. This article used qualitative descriptive research methods. Qualitative descriptive research methods seek to answer the "what," "how," or "why" questions related to the phenomenon under study. The aim is to understand the research subject deeply and not generalise the results to the wider population. This research article concludes the principle of state responsibility, which essentially contains the obligation of states that have an impact on other countries to make reparation to the aggrieved country and restore the condition of the concerned country. In Canada, there were frequent forest fires in previous years, causing haze that spread to various countries. By understanding the consequences of forest fires and haze spread, Indonesia should enhance its prevention and management strategies by adopting approaches from Canada's forest fire management. The Trail Smelter case serves as a benchmark for addressing haze pollution, and Canada's experience offers valuable lessons for Indonesia, which also faces similar wildfire risks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.043 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".