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Record W4399815260 · doi:10.32670/ecoopsday.v5i1.4180

Menggali Potensi Tanaman Herbal Sebagai Alternatif Pengobatan Hipertensi

2024· article· en· W4399815260 on OpenAlexaff
Siti Nuraeni, Sri Wulan Megawati, Willfridus Demetrius Siga, Silpi Pebriawati, Widya Nurasih, Acep Agung Nugraha

Bibliographic record

VenueE-coops-day. · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMedicinal Plant Research
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsMedicineTraditional medicineRural communitySocioeconomicsSociology

Abstract

fetched live from OpenAlex

This activity (PKM) aims to provide education to the community regarding herbal medicine for hypertension at Posyandu Anggrek 4 Sukamandi Village which was held on 26 August 2023. This activity is one of the prevention efforts related to hypertension which is increasing in rural areas. The participants of this PKM are the people of Dusun 4 Sukamandi Village, totaling 30 people. This activity was carried out offline by using lecture, question and answer and student demonstration methods related to how to make alternative herbal drinks for the prevention of hypertension. Herbal plants used as an alternative treatment for hypertension are pandan leaves and celery. For plant processing, it is done by boiling the plant with a time of around 3-5 minutes. This activity aims to provide education about the prevention of hypertension in the community, with this counselling activity there is a significant increase in community knowledge with an average score of 42.00 suggestions for this activity are the involvement of other partners as supporters still need to be improved. In addition, the time for implementing activities needs to be increased so that the objectives of the activity can be fully achieved.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0340.003

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 head0.028
GPT teacher head0.261
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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