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Record W7042426844

Pola Perpindahan Unsur Hara dan Bahan Organik Pada Sawah Berteras di Kelurahan Limau Manis, Kota Padang

2024· other· id· W7042426844 on OpenAlexaboutno aff

Bibliographic record

VenueAndalas University eThesis (Andalas University) · 2024
Typeother
Languageid
FieldAgricultural and Biological Sciences
TopicPlant Growth and Agriculture Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsNitrogenHydrology (agriculture)Quarter (Canadian coin)Air pollution
DOInot available

Abstract

fetched live from OpenAlex

Pola perpindahan unsur hara pada tanah sawah berteras berbeda dengan sawah tidak berteras. Penelitian dilakukan untuk mengetahui pola perpindahan unsur hara di lahan sawah berteras pada lahan sawah intensif di Limau Manis Kota Padang selama satu musim tanam. Contoh tanah diambil dari empat petak teras pada lapisan olah (±15 cm), satu titik per petak. Contoh air juga dikumpulkan dari saluran masuk dan keluar setiap teras pada tiga waktu yang berbeda: saat pengolahan tanah, 10 hari setelah pemupukan, dan saat penyiangan. Parameter yang dianalisis adalah N�Total, P-Tersedia, K-dd, C-organik, KTK, pH, BV, Nitrat, Fosfat, dan Kalium. Hasil penelitian menunjukkan bahwa jumlah sedimen yang diangkut menurun dari 6,288 kg/ha di teras pertama menjadi 2,285 kg/ha di teras keempat. Pada tanah awal unsur hara menunjukkan variasi perpindahan: N-Total dan P-Tersedia meningkat dari teras pertama ke teras keempat, sementara K-dd cenderung menurun. Pada air irigasi, nitrat dan fosfat meningkat dari teras pertama ke teras keempat, dengan konsentrasi nitrat tertinggi tercatat saat pemupukan, fosfat tertinggi saat pengolahan tanah, dan kalium tertinggi saat penyiangan. Pada sedimen, nitrogen dan fosfor menurun dari petakan atas ke bawah, dan nitrogen yang berpindah tidak sebanyak bahan organik. Kalium juga mengalami penurunan dari 0,004 kg/ha di petakan atas menjadi 0,002 kg/ha di petakan bawah. Berdasarkan hasil penelitian ini, disarankan agar petani menutup saluran air pada saat pemberian pupuk untuk meningkatkan efisiensi penggunaan pupuk dan hasil panen.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.285
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.008
GPT teacher head0.165
Teacher spread0.157 · 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 designNot applicable
Domainnot available
GenreOther

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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