Data Envelopment Analysis and Bootstrap Approaches for Efficiency Measure of the Autonomous Port of Dakar
Bibliographic record
Abstract
In this article, we measure the e_ciency of the Autonomous Port of Dakar (APD) and identify the causes of inefficiency for the year 2021. Measuring port e_ciency is an important factor in strengthening its competitiveness and stimulating national development. In the literature, the most widely used methods for measuring e_ciency in the port sector are parametric and non-parametric methods. Our objective is to apply Data Envelopment Analysis (DEA) with two models, namely: the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) to determine the efficiency scores and the bootstrap approach the Simar and Wilson in 2007 to correct the errors and determine the confidence intervals. Numerical simulations are performed in the two distinct zones separated by a Fishing Port (FP), Naval Repair Workshops (NRW) and the Military Zone (MZ), while the others zones are the Decision-Making Units (DMUs), detailed in table 2. The results show that APD obtains six (6) e_ective DMUs with CCR model (average score of 0.858) and ten (10) effiective DMUs with BCC model (average score of 0.951). The average scale efficiency is 0.897. With the bootstrap approach, we obtain an average bias-corrected of 0.700 for CCR model (with confidence intervals of [0.324; 0.1291]) and 0.870 for BCC model (with confidence intervals of [0.620; 1.197]). These results will allow the decision makers of the Dakar port authority to improve its performance and competitiveness at the national and international levels.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".