Environmental and Health Benefits from Designating the Marmara Sea and the Turkish Straits as an Emission Control Area (ECA)
Bibliographic record
Abstract
Ship emissions degrade air quality and affect human health, and are increasingly becoming a matter of concern. Sulfur emission control areas (ECA), specific coastal regions where only low-sulfur fuels may be consumed by ocean-going ships, have proven to be useful tools to reduce ship-sourced air pollution along the North American, Canadian, and European North and Baltic Sea coastlines. The present work assesses the environmental and health benefits which would derive from designating an ECA in the Marmara Sea and the Turkish Straits (50 000 ships/year; 23 million inhabitants). Results show evidence that implementing an ECA would be technically viable and that it would reduce ship-sourced PM10 and PM2.5 ambient concentrations in Istanbul by 67%, and SO2 by 90%. The reduction of the air pollution burden on health was quantified as 210 hospital admissions from exposure to PM10, 290 hospital admissions from exposure to SO2, and up to 30 premature deaths annually due to ECA emission controls. Consequently, the designation of an ECA in the Marmara Sea and the Turkish Straits is evaluated as a positive, technically viable and real-world measure to reduce air pollution from ships in Turkey.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".