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Record W4386627501 · doi:10.53555/sfs.v9i1.1595

Use Of Pesticides Or Harmful Substances In Fish Drying: A Study On The Coast Of Purba Medinipur District, West Bengal

2022· article· en· W4386627501 on OpenAlexvenueno aff
Suman Kalyan Samanta, Arnab K. Ojha

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFisheries and Aquaculture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLivelihoodBENGALCasteWet seasonWest bengalDry seasonFish <Actinopterygii>FisheryFishingSocioeconomicsWork (physics)GeographyAgricultureBiologyEngineeringPolitical science

Abstract

fetched live from OpenAlex

Fishing is one of the major livelihood activities of traditional fishermen in the coastal region of Purba Medinipur district in West Bengal. Most of these traditional fishermen are lower-caste Hindus. In the traditional caste hierarchy, these traditional Hindu fishermen face various kinds of discrimination and unlawful activities. Poverty is their common feature. A large number of traditional fishermen depend on the traditional petty trade of fish drying. Earlier, they used only traditional technologies and did not depend on harmful substances (especially various pesticides or insecticides) to dry or preserve fish in a short time. Recently, the technology and methods of drying fish have changed a lot for fishermen in coastal regions. Nowadays, traditional fishermen often use various harmful substances such as pesticides or insecticides to dry the fish quickly, to preserve it for a longer period and to dry it during the rainy season or in other humid climatic conditions. Most of these substances have serious health impacts. The present work attempted to determine the health impacts of fish drying workers who work in khoti areas (fish drying centres where fishermen are engaged in fish drying) and are involved to some extent in this chemical-driven fish drying process. The study was conducted among the traditional fishermen engaged in fish drying and petty trading in different khoti areas of Purba Medinipur district in West Bengal. It is mainly a field observation-based work in which various traditional methods of fieldwork were used with due importance

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.296
GPT teacher head0.280
Teacher spread0.015 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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