Characterization of Airfreight-Related Logistics Firms in the City of Cape Town, South Africa
Bibliographic record
Abstract
Background: Airports are essential drivers of spatial development; hence the placement of logistics facilities relative to airports is a topical subject. Despite the wealth of the literature on the subject, relatively little is known about the airfreight catchment of airports. To contribute to the existing knowledge, the paper used the study area of the City of Cape Town municipality, South Africa, to address three research objectives, namely analysis of factors that influence the placement of logistics firms in the municipality, analysis of the linkages of the logistics firms with Cape Town International Airport (CTIA), and analysis of the association between airfreight-related firms and the general attributes of logistics firms in the municipality. Methods: The study hinged on a quantitative design, which included a survey and spatial analysis. A total of 110 logistics firms were sampled through a stratified random sampling technique, and 66 firms participated in the telephonic interviews conducted in October and November 2021. Survey data were analyzed using Stata, and spatial analysis was undertaken using ArcGIS 10.8 and QGIS 3.16. Results: It was discovered that a quarter of the respondent logistics firms utilized CTIA for airfreight purposes. At a municipal scale, the potential airfreight catchment of CTIA extended to about a 20 km radius of the airport. Conclusions: In formulating the spatial plans, the planning authorities are encouraged to take cognizance of the possible extent of the catchment, wherein airfreight-related firms do not necessarily locate near the airport.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".