Aquaculture in coastal urbanized areas: A comparative review of the challenges posed by Harmful Algal Blooms
Bibliographic record
Abstract
Increasing global population has resulted in increased urbanization of coastal areas across the globe. Such an increase generates many challenges for sustainable food production and food security. The development of aquaculture has proven to be an extremely good option to ensure food security (uninterrupted supply and good quality of food) by many countries, especially those with urban areas affected by space limitations such as Singapore. However, the implementation of aquaculture is not without its challenges and impacts to the environment, with Harmful Algal Blooms (HABs) being one of the major concerns in coastal waters. In this review we analyze the development of the aquaculture industry with respect to HABs in Singapore and compare it to similar urban areas such as Hong Kong (SAR China), Salalah (Oman), Cape Town (South Africa), Valencia (Spain), Rotterdam (The Netherlands), Tampa bay (USA), Vancouver (Canada), and Sydney (Australia). Along with HABs, the abovementioned urban areas face different challenges in sustainably increasing their aquaculture production with respect to the economy and geography. This review further assesses the different production and monitoring strategies that have been implemented to counter these challenges while sustainably increasing production. The ongoing COVID-19 pandemic has affected the world with lockdowns and border closures resulting in logistical difficulties in seafood trade which has further accentuated the dependencies on food import. We conclude that the challenges faced by urban areas for sustainable achievement of food security through development of the aquaculture industry can be effectively managed through proper planning, management and collaboration of knowledge/skills on an international level.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".