Flood-related risks in Ho Chi Minh City and ways of mitigation
Bibliographic record
Abstract
With an ever-growing population of around 10 million inhabitants (officially 7.9 in 2013), Ho Chi Minh City (HCMC) is set to become one of the largest cities in South East Asia and already occupies a major economic role in the area. To accommodate the increasing population, the megacity now stretches out in an urban continuum covering more than 800 square kilometers and is currently growing at a rate of 3.2% per year. If the neighboring provinces around HCMC are included, the total population reaches nearly 18 million people. This paper attempts to describe the interplay between HCMC and flood-related risks and offer some guidelines to deal with inundations. The potential risks of flooding by rain, tsunami and/or dam failure upstream of the city are evaluated and contextualized within the perspective of climate and human-induced environmental changes. The region is highly vulnerable to the combined effects of subsidence and rising sea levels and has already led to serious flooding that may extend spatially before the end of the century. We propose possible preventative solutions to urban flooding using a multi-pronged approach to issues regarding urban development and suggest a redevelopment strategy for major infrastructure projects.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".