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Pelatihan Pembuatan Pupuk Biosaka, Upaya Perlindungan Tanaman Berbasis Ekologi Untuk Menjaga Kelestarian Lingkungan

2024· article· en· W4401480091 on OpenAlexaff
Saryanto Saryanto, Rejo Kirono, Khotim Hanifudin Najib, Ahdan Aufa Ilman, Ainun Jaria, Devi Rahmaningtias, Madiasta Madiasta, Margaretha Eltris Don, Nurul Nur Amelia, Heri Winarno, Lono Madyo

Bibliographic record

VenueSolusi Bersama · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Development and Management
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsFertilizerEnvironmentally friendlyAgricultureOrganic fertilizerProduction (economics)Agricultural engineeringAgricultural scienceEnvironmental scienceBusinessEngineeringAgronomyBiologyEconomicsEcology

Abstract

fetched live from OpenAlex

Biosaka fertilizer production training is an educational program designed to provide practical knowledge and skills in the production of organic fertilizer through fermentation of natural ingredients. Biosaccharine fertilizer, which is made from plant residues, animal waste and certain microorganisms, offers an environmentally friendly and efficient solution for improving soil fertility and plant health. This training includes an introduction to materials and tools, the fermentation process, and the application of biosaccharide fertilizer to various types of plants. Through this approach, participants—including farmers, agricultural extension workers, and other individuals—are expected to be able to produce high-quality biochemical fertilizer independently. It is hoped that the results of this training will reduce dependence on chemical fertilizers and support sustainable agricultural practices that are more economical and environmentally friendly.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.003

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 head0.009
GPT teacher head0.186
Teacher spread0.177 · 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 designNot applicable
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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