Adoption of a multi-criteria approach for the selection of operational measures in a maritime environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent times, there have been developments in the maritime industry that underscore the need to optimise operations to yield maximum productivity. Apart from this, stakeholders in this industry have also advocated improvements in seaport operations' critical areas. However, there is no known study in which the relationship between performance criteria and seaport operation measures is investigated. This study proposes a framework for selecting operation measures for the maritime industry. It uses stakeholders' expectations for operational criteria and fuzzy logic to design the framework. Nine criteria were considered in the framework, while Fuzzy VIKOR (VIsekriterijumska optimizacija I KOmpromisno Resenje) and fuzzy Shannon entropy were incorporated into it. The framework's applicability was tested using information that was obtained from Tin can port, Lagos, Nigeria. During this process, hinterland traffic diversion (A1), congestion pricing (A2), off-dock container yards (A3), Fast rail shuttles (A4), expanded rail connections (A5) were considered as alternatives for seaport operational measures. When the developed framework was used to analyse the collected information from Tin Can port, Lagos, Nigeria, the fuzzy VIKOR index ranked the alternatives as A1 » A2 » A3 » A5 » A4. Therefore, this study's insights show that mathematical models can be used to make informed seaports decisions.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it