PERTANGGUNGJAWABAN KANADA ATAS POLUSI UDARA DI KOTA NEW YORK BARDASARKAN CONVENTION ON LONG-RANGE TRANSBOUNDARY AIR POLLUTION 1979
Bibliographic record
Abstract
Forest fires have become an annual disaster that continues to occur in various parts of the world. The 2023 forest fires in Canada were considered severe. The 2023 wildfire season in Canada was extraordinary, with more than 5,700 fire incidents having burned 13.7 million hectares by August 16, 2023, since the start of the fire season. The identified issue is whether Canada should be held responsible for air pollution in New York under the 1979 Convention on Long-Range Transboundary Air Pollution (CLRTAP) and whether the measures taken by Canada to address air pollution in New York have been in accordance with the provisions of the 1979 CLRTAP. The method employed is normative legal research, a descriptive-analytical approach that utilizes secondary data, incorporates qualitative data analysis, and employs deductive reasoning. The results of the study indicate that Canada should be held responsible for the air pollution in New York City, as Canada has fulfilled the elements of state responsibility as stipulated by the 1979 CLRTAP, and that Canada’s measures have complied with the convention through international cooperation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".