Holistic evaluation of the environmental impacts of shipping in the sensitive region of Ria de Aveiro
Bibliographic record
Abstract
Shipping activity can be a substantial source of pollution and impact on the environment, including air, water and ecosystems, as well as adverse health and climatic effects. Due to the distribution of maritime transport activity routes in the EU, a large portion of the population is exposed to shipping pollution throughout Europe. The ongoing European project EMERGE aims to investigate and quantify these impacts over Europe, and in more detail, in specific case studies regions. The Aveiro lagoon region in Portugal is one of these case studies. This region is a Natura 2000 area, and also includes a medium-sized port. Both air quality and water modelling tools were applied to assess the impact of the emissions and discharges from shipping (to air and water) in the region in 2018. Additionally, ecotoxicological impacts were determined by bioassays to evaluate the impact of scrubber-water discharges on the most sensitive stages of marine invertebrates, and on the post-exposure feeding inhibition of crustacean and bivalve species. The results show that there was a substantial increase in atmospheric pollutant concentrations due to emissions attributed to shipping, which was most relevant for NOx and SO2 (up to a 30 % shipping contribution). There was no significant degradation of the water quality, mainly as the ships operating in this area did not have scrubber equipment. The ecotoxicological tests were performed with three samples of scrubber water, including one artificial sample and two samples collected on-board ships. If scrubber water would have been discharged in this area, the results indicated that the majority of the tested species would be exposed to lowest observed effect concentration (LOEC) for the different scrubber-water samples, as well as to substantial concentrations of metals, PAHs, and alkylated PAHs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".