Sea cucumber conservation in Palk Bay and Gulf of Mannar, India "An evaluation of the \ncurrent conservation measures on sea cucumber stocks in \nPalk Bay and Gulf of Mannar of India"
Bibliographic record
Abstract
Sea cucumber fishery and trade were one of the top non-finfish income streams for the coastal \npeople of Palk Bay and Gulf of Mannar in the South East coast of India. As there was no regulation to \ncontrol the fishery, there was a concern on decline in sea cucumber populations. In order to \nconserve the over-exploited stocks, the Ministry of Environment, Forestry and Climate Change, \nGovernment of India banned the fishery and trade of sea cucumbers by including them under Wild \nLife Protection Act 1972 since 2001. The enforcement of a blanket ban of sea cucumber fishing over \nthe last 14 years might have helped in reviving their populations; at the same time, the ban would \npossibly had a social and economic impact on scores of people, who were dependent on the sea \ncucumber fishery. To understand the situation, the Bay of Bengal Large Marine Ecosystem (BOBLME) \nproject approved a short term project to Central Marine Fisheries Research Institute (India). The \nproject was intended to understand the sea cucumber stocks and implications of the ban on the \nlivelihood of fishers in Palk Bay and Gulf of Mannar. The purpose of the project was also to suggest \nmanagement options for conservation and sustainable use of sea cucumber resources.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".