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Identifikasi Unsur Hara Sulfur pada Sistem Irigasi Primer di Tanah Sawah Wilayah Bendungan Arca Kiri, Kabupaten Banyumas

2021· article· id· W3209300594 on OpenAlexaff
Leony Agustine, Amanullah Thaariqa Tri Wibowo, Begananda Begananda

Bibliographic record

VenueJurnal Ilmiah Teknologi Pertanian Agrotechno · 2021
Typearticle
Languageid
FieldAgricultural and Biological Sciences
TopicPlant Growth and Agriculture Techniques
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsForestryEnvironmental sciencePhysicsGeography

Abstract

fetched live from OpenAlex

This study aims to determine1) the amount of nutrient content of Sulphur in a paddy field in the irrigation channel of Arca Kirit dam in Banyumas Regency, 2) nutrient distribution of Sulphur in paddy field in the irrigation channel of Arca Kiri dam in Banyumas Regency, and 3) recommendations of Sulphur fertilizers in paddy field in the irrigation channel of Arca Kiri dam in Banyumas Regency. The research was conducted in paddy field, in the irrigation channel of Arca Kiri dam in Banyumas Regency, then continued with analysis of the soil in the Institute of Determining Technology of Agriculture (BPTP), Yogyakarta. The research carried out by the determination of sample points based on a purposive random sampling be based Land Homogenous Unit (SLH). Land Homogenous Unit arranged by overlaying the Administrative Map, Slope Map, and Soil Type Map. The number of samples is 8, which is located in 5 villages. The variables were observed namely the nutrient content of Sulphur, EC, pH (H2O), and BS of land. The results showed that the nutrient content of Sulphur from both SLH relatively high, there were SLH A1 reach 86.20 ppm and SLH A2 reach 85.33 ppm. Sulphur fertilizer recommendations for SLH A1 is 10.64 kg S/ha and SLH A2 is 14.60 kg S/ha.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.215
Teacher spread0.197 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

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