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Record W4402523403 · doi:10.1186/s41072-024-00182-z

Toward an efficient sea-rail intermodal transportation system: a systematic literature review

2024· article· en· W4402523403 on OpenAlexafffund
Tareq Abu-Aisha, Jean‐François Audy, Mustapha Ouhimmou

Bibliographic record

VenueJournal of Shipping and Trade · 2024
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Trois-Rivières
FundersMitacs
KeywordsTransport engineeringPort (circuit theory)Context (archaeology)Linkage (software)Transportation planningBusinessEngineeringGeography

Abstract

fetched live from OpenAlex

Abstract Effective ground transportation modes linkage with the seaport plays a crucial role in facilitating smooth cargo movement from marine transportation mode to the inland areas and vice versa. Unlike road transportation, rail linkage is a cost-effective and environmentally friendly option. Inadequate sea rail connectivity within the seaport hampers cargo movement speed and impacts overall port capacity. This systematic review places emphasis on sea rail intermodal transportation at the seaport. The review categorizes and analyses previous research contributions to the sea-rail intermodal transportation system, and is organized into five categories: performance evaluation, problem-solving methodologies, planning issues, factors affecting sea rail intermodal transportation, and enhancement strategies within the context of sea rail intermodal transportation. The study discerns current research patterns and identifies gaps within the existing literature while also offering insights into potential future research avenues.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.046
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0200.017
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.229
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations16
Published2024
Admission routes2
Has abstractyes

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