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Record W4416310947 · doi:10.22441/sinergi.2025.3.002

Port perspectives in a changing climate: strategies for enduring impact

2025· article· W4416310947 on OpenAlexaff
Astina Tugi, Nazirah Mohamad Abdullah, Ami Hassan Md Din, Badrul Hisham Ismail

Bibliographic record

VenueSINERGI · 2025
Typearticle
Language
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsCanadian Hydrographic Service
FundersUniversiti Tun Hussein Onn Malaysia
KeywordsPort (circuit theory)Adaptation (eye)Climate changeResilience (materials science)SustainabilityFlooding (psychology)Psychological resilienceClimate change adaptation

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.024
GPT teacher head0.322
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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