Impacts of large-scale seaweed farming trends on the local environment and community
Bibliographic record
Abstract
Seaweed farming has been growing globally as a sustainable food production method and by expanding applications for human health to agricultural and industrial materials. Recent literatures published within ten years were examined with some keywords including “large-scale seaweed farming” and “seaweed industrialization.” This comprehensive literature review on large-scale seaweed farming suggested that there are both concerns and opportunities regarding ecological-environmental and socio-economical perspectives brought by such current growing trend. While negative impact on other concurrent natural organisms, pollution, and disease and pest outbreaks, are concerning, seaweed farming can also contribute to water quality and carbon sequestration. Moreover, seaweed farming needs to consider producers’ economical conditions and health, as well as potential environmental burden by suboptimal farming conditions. There are technological challenges to meet the growing demand, while seaweed farming has potential value in food security and women empowerment. Considering these multiple impacts generated by upscaling seaweed farming, global actions to maximize benefits and mitigate negative consequences is necessary. It is crucial to enhance capacities and develop technologies for production and processing. The insufficient coverage of existing international and regional frameworks for biosecurity of aquaculture requires improvement of disease monitoring and surveillance mechanism along with increasing investments in research and capacity building. They also need consistent terminology and optimized local-level practices to address multiple challenges including social and human aspects, which can provide valuable knowledge for sustainable development.
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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 | 0.002 |
| 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 | 0.000 | 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".