The Correlation Between Asian Port Cities and Traditional Portuguese Urban Forms Based on Map and Machine Learning Analyses
Bibliographic record
Abstract
In the 16 th and 17 th centuries, under the influence of the Portuguese Empire’s overseas expansion and cultural integration, the island city of Macau became an important international trading port in the Eastern Sea, with close ties to the Asian port cities governed by Portugal. This study introduces a new method for comparing urban morphological layouts using machine learning and investigates the potential benefits of combining urban morphological analysis with machine learning techniques. In addition, a combination of urban morphology theory and machine learning is used to excise samples of urban morphology from Portuguese urban geographical information maps. The morphological characteristics of port city areas are further extracted, and training labels for typical Portuguese urban textures are established. Using the YOLOv4 object detection algorithm, the results are compared with the urban textures of typical island and port cities of the Asian Silk Road—Goa in India, Malacca in Malaysia, Macau in China, and Dili in Timor-Leste—revealing the similarities and differences among the port cities in Asia influenced by traditional Portuguese urban practices. The results reveal the relationship between maritime trade and urban form.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".