Assessment of Container Terminal Operations in South-Western Ports in Nigeria
Bibliographic record
Abstract
Containers, a global maritime trade influencer, have increased the importance of ports. Ports serve as an economic catalyst for revenue and employment. The importance of container transport cannot be overemphasized in international trade especially in Nigeria. Hence, it becomes expedients to assess the elements of container terminal capacity, analyse the trend of cargo throughput and as well determine the relationship between handling equipment and cargo dwell time in Western ports in Nigeria. The study adopts multistage sampling. Also, both primary and secondary data were collected from the staff of the container terminal operators in Tincan Island Port Complex and Apapa Port Complex and 347 respondents were chosen using Yamene formular. Furthermore, Descriptive statistics such as frequency table, line graph, bar charts were used to examine the elements of container terminal capacity, analyse the trend of cargo throughput. The inferential statistics such as regression analysis was used examines the relationship between thandling equipment and cargo dwell time. The result showed a decline in cargo throughput from 2019 to 2020, a trend that can be directly attributed to the COVID-19 pandemic. Also, it was shown that the coefficient of handling equipment is 0.725, indicating that for every unit increase in the handling equipment score, the cargo dwell time increases by 0.725 units. The t-value of 21.558 and a significance level of 0.000 signify that the handling equipment is a significant predictor of cargo dwell time. Based on the findings of the study, it was concluded that terminal space, handling equipment, daily stock and port labour were significant factors or elements of container terminal capacity. It was recommended that Nigerian Ports Authority should invest more on sustainable infrastructure and terminal operators and stevedoring company should train and retrain their staff on modern handling equipment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".