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Record W4391793804 · doi:10.53555/sfs.v10i1s.2301

Identification Of Different Indigenous Technical Knowledge Application In Agriculture And Allied Sector In Some Selected Areas Of West Bengal

2023· article· en· W4391793804 on OpenAlexvenueno aff
Sahely Kanthal, Suman Garai

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIndigenous Knowledge Systems and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsWest bengalIndigenousIdentification (biology)AgricultureTraditional knowledgeGeographyBENGALSocioeconomicsSociologyBiologyBotanyEcologyArchaeology

Abstract

fetched live from OpenAlex

Traditional knowledge or Indigenous Technical Knowledge (ITK) is the knowledge that people in a given community have developed over time, and continue to develop. It is based on experience. Often tested over long period of use, adopted to local use and environment, dynamic and changing, and lays emphasis on minimizing risks rather than maximizing profits. The studies conducted on three blocks (Mahammad Bazar, Sainthia, Bolpur Sriniketan block) of Birbhum district for documentation of traditional knowledge in different sector. Ninety respondents from nine villages are randomly selected and simple random sampling techniques followed for this study. The data were collected through direct observation, group interview, tapping the knowledge of elderly women and young boys. The traditional knowledge is documented in agriculture, animal husbandry and allied sector. In agricultural sector traditional knowledge are practiced from seed germination to post harvest management. In animal husbandry traditional knowledge is used from milk production to disease control and traditional implements were also catalogued with their use and status and medicinal plans which are used in formulation of different ethno medicinal preparation for curing various types of diseases and ailments. This indigenous knowledge regarded as environment friendly, ecological sound, location specific and cost effective. Farmers are using these different technologies through generation-to-generation and few of them have learned to use these technologies from the neighbours. Studies on ITK shows the way for incorporating ITK and scientific knowledge for development of eco-friendly technologies for sustainable agriculture and for development of technologies that would be more need based, better problem solving, locally applicable, easily acceptable and more convincing to the farmers.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.268
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.051
GPT teacher head0.237
Teacher spread0.186 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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