Planetary Health Consequences of Telecoupled Shrimp Farming
Bibliographic record
Abstract
The international demand for shrimp from Bangladesh exhibits a Telecoupled system. Semi-intensive to intensive shrimp farming has changed vast coastal areas into saline zones by altering land use and land cover. While shrimp cultivation significantly contributes to foreign exchange earnings, it also leads to various social and environmental impacts that affect planetary health. This study sees shrimp farming as a result of these Telecoupled dynamics. It uses a mixed-methods approach, combining both primary and secondary data to examine its effects on planetary health in Bangladesh's southwestern coastal areas. The findings reveal several important health and social issues associated with shrimp farming. These include (i) scarcity of drinking and household water, (ii) infectious diseases, (iii) non-infectious diseases, (iv) food and nutritional insecurity, (v) antimicrobial resistance and chemical contamination, (vi) mental pressure, (vii) disaster-related health vulnerability, (viii) social conflict, (ix) healthcare inequality, and (x) rural-urban migration. This analysis enhances our understanding of the complex interactions between humans and nature in shrimp farming systems and their evolving impacts on planetary health in southwestern coastal Bangladesh. The study stresses the urgent need for integrated, ecosystem-based agricultural practices to find a balance between economic benefits and sustainable health and environmental outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".