Smart Port: An Upshot of the Maritime Sector's Digital Transformation
Bibliographic record
Abstract
With the emergence of the Fourth Industrial Revolution, organizational survival has become more and more in danger. Digital transformation (DT) is undoubtedly influencing the strategic parts of any company or state. Therefore, it remains important to integrate it into several processes and procedures to achieve better outcomes in all aspects. Since no industry has escaped from DT impacts, the maritime sector sprung its digital journey. As a result of this adventure, the concept of "Smart port" arises to address the majority of the issues that the maritime sector faces, such as increasing pressure from global trade demands and the emergence of digital transformation. Smart ports, with their advanced technologies and data-driven operations, can alleviate this pressure. In this sense, our work provides a prospective review of the theoretical and empirical works dealing with smart ports. It underlines the main drivers, barriers, and best practices related to smart ports. It can thus serve as a guide for companies and ports willing to start their digital journey.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".