Port perspectives in a changing climate: strategies for enduring impact
Bibliographic record
Abstract
More than 50% of the world's trade happens via seaborne line. The sustainability of the ports is crucial as it boosts economic growth. However, climate change and its effects have disturbed the port's activities. This paper highlighted the possibility of climate change effects threatening some ports worldwide. A systematic literature review has been conducted, and 11 resources have been used to summarize their impact on port activities. The climate change effects encountered by the ports and their authorities' adaptation measures are underlined. Ports worldwide are considered. As a result, sea level rise (SLR), storm surges, and flooding are some threats that can affect port activities. Adaptation and mitigation plans can be more successfully implemented with excellent knowledge of the factors leading to increased exposure. From the port expansion to creating a new location of ports, the other mitigation and adaptation plan towards the sustainable ports is by providing an accurate topographic map, a good simulation software, and good resilience infrastructures and adaptation framework. The related ports have suggested and implemented adaptation and mitigation approaches to resolve the problem and sustain their ports and harbour activities. Adaptation and mitigation measures taken will respond to the effects of climate change.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".