RETRACTED: Uncovering water quality and evaluating vulnerabilities of small-scale fisheries in Chilika Lagoon, India
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Small-scale fisheries (SSFs) play a strong role in sustaining millions of livelihoods, food security, nutrition, and income globally but the fishers engaged in this sector simultaneously experience high levels of vulnerability and processes of marginalisation. Several factors are attributed to the multidimensional vulnerabilities the small-scale fishers experience, spanning both natural (e.g., natural disasters, ecosystem change) and anthropogenic (e.g., policy change, hydrological interventions, aquaculture) pressures. While there is much literature on various natural and human drivers of vulnerability in small-scale fishery communities, an absence of research connecting vulnerability with water quality is evident. Fisher communities often talk about fish in relation to the health of their aquatic habitats wherein water quality is seen as a key parameter. The link between healthy fish and good quality water has significant implications for strong and viable fishing communities. This paper examines these links further by focusing on the nature of vulnerabilities caused by water quality changes in the small-scale fishery system of Chilika Lagoon in India. We undertake detailed analysis of the invasive shrimp aquaculture activities and hydrological interventions for opening of a lagoon inlet with the Bay of Bengal as two dominant drivers adversely impacting water quality and increasing vulnerabilities of the entire small-scale fisheries social-ecological system. Our analysis suggests that there are strong interconnections between changes in water quality and the levels of vulnerabilities in the SSFs of Chilika Lagoon. Pollutants such as pesticides, and organic compounds accumulate in fish tissues and affect their growth, reproduction, and overall health. This led to declines in fish populations, making it more difficult for fishers to make a living. In addition to direct impacts on fish populations, poor water quality also has indirect effects on the social and economic vulnerability of SSFs. For example, contamination of water sources led to the reduced number of fish species reducing the amount of time fishers can spend on fishing activities. This also affected the marketability of fish products, reduced income and increased poverty. To fully understand the interconnections between water quality and vulnerability in SSFs in Chilika Lagoon, it is important to consider both environmental and social factors, as well as the complex feedback loops between these factors. The study helps in bridging a crucial gap in our understanding of the role of water quality in vulnerability analysis within resource dependent communities. We conclude with key insights on possible coping responses and adaptive capacity necessary for the small-scale fisheries communities to transition toward viability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".