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M Pengembangan Pompa Irigasi Pertanian Menggunakan Energi Listrik Tenaga Surya di Desa Duri, Slahung, Ponorogo

2021· article· en· W3169213865 on OpenAlexaff
Didik Riyanto, Yoyok Winardi, Mohammad Muhsin

Bibliographic record

VenueAgrokreatif Jurnal Ilmiah Pengabdian kepada Masyarakat · 2021
Typearticle
Languageen
FieldEngineering
TopicEngineering and Technology Innovations
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsIrrigationAgricultural engineeringAgricultureEnvironmental scienceAgricultural machineryWater resource managementEngineeringAgricultural scienceEnvironmental engineeringGeographyAgronomy

Abstract

fetched live from OpenAlex

Community service program To develop agricultural irrigation is carried out in Duri village, Slahung sub-district, Ponorogo district. Agriculture in Duri village has been dependent on irrigation from the rain so agricultural activities only rely on the availability of water during the rainy season, while the potential for water from groundwater with a depth of 20 m has not been able to be utilized optimally because it is constrained by relatively expensive equipment and technology. For this reason, a community service program is carried out which aims to meet the irrigation needs of farmers. The method of implementing activities consists of an inventory of data related to the conditions and needs of materials to be applied with technology, technology design, technology application, training in the use of technology, and evaluation. The result of the activity is a program of implementing an irrigation pump with solar cell electricity that is applied to one of the farmer groups' fields, namely a water pump that is able to lift water from a depth of 20 m with a water discharge of 1080 L/hour and is able to last for 2 hours. able to meet water needs in the dry season and can be used to grow crops on a small scale such as vegetable crops on a land area of 100 m.

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: none
Teacher disagreement score0.060
Threshold uncertainty score0.200

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0600.006

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 head0.007
GPT teacher head0.190
Teacher spread0.183 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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