Exploring Water Quality as a Determinant of Small-Scale Fisheries Vulnerability
Bibliographic record
Abstract
Water quality is a fundamental indicator of coastal ecosystem health. Maintaining appropriate levels of water quality is critical for the growth of aquatic species and the livelihoods of dependent small-scale fishery (SSF) communities. However, natural (e.g., cyclones, floods) and hu-man-induced (e.g., hydrological changes, varied fishing techniques) factors create cumulative stress on these systems, leading to environmental and socioeconomic challenges. This often manifests as food insecurity, occupational displacement, and biodiversity loss. Despite existing research on coastal sustainability and resilience, the intricate connection between water-quality variations and social–ecological vulnerabilities remains understudied. This paper addresses this gap, focusing on the interplay between water quality changes and the vulnerabilities faced by SSF communities. Using the Chilika Lagoon in India as a case study, this synthesis paper examines water-quality processes and their impact on community vulnerabilities over three decades. It analyses various coping and adaptive responses of the fisher communities and the potential of their actions for creating viable small-scale fisheries. Our findings suggest ways in which SSF communities can respond to these vulnerabilities and help foster knowledge for their transition to viability.
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 | 0.001 | 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) | 0.002 | 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".