IDENTIFIKASI SIFAT FISIKA TANAH ULTISOLS PADA DUA TIPE PENGGUNAAN LAHAN DI DESA BETENUNG KECAMATAN NANGA TAYAP KABUPATEN KETAPANG
Bibliographic record
Abstract
Penelitian bertujuan membandingkan sifat fisika tanah pada kebun karet dan kelapa sawit di Desa Betenung Kecamatan Nanga Tayap Kabupaten Ketapang. Sampel tanah diambil secara diagonal dengan mengambil sampel tanah utuh, sampel tanah agregat utuh dan contoh tanah terganggu. Sampel tanah di ambil pada kebun karet dan kelapa sawit dengan kedalaman 0-30 cm dan 30-60 cm. Hasil penelitan menunjukkan profil warna tanah pada kebun karet terdapat dua lapisan warna tanah yaitu 7,5YR 3/3 coklat gelap dan 7,5YR 6/8 kuning kemerahan. Profil warna tanah pada perkebunan kelapa sawit terdapat dua lapisan warna tanah, lapisan pertama 7,5YR 4/6 coklat gelap, lapisan kedua 7,5YR 6/8 kuning kemerahan. Struktur tanah kebun karet lapisan I remah, lapisan II dan III gumpal membulat. Struktur pada kebun kelapa sawit lapisan I remah, lapisan II gumpal bersudut, lapisan III gumpal membulat. Tekstur tanah kebun karet kedalaman 0-30 cm dan 30-60 cm termasuk lempung dan lempung berliat, pada kebun kelapa sawit kedalaman 0-30 cm dan 30-60 cm termasuk lempung liat berdebu. Hasil uji t bobot isi tanah kebun karet dan kelapa sawit kedalaman 0-30cm berbeda tidak nyata, kedalaman 30-60 cm berbeda nyata. Hasil uji t kadar air kapasitas lapangan kebun karet dan kelapa sawit kedalaman 0-30 cm dan 30-60cm berbeda tidak nyata. Hasil uji t porositas tanah kebun karet dan kelapa sawit kedalaman 0-30 dan 30-60 cm berbeda tidak nyata. Hasil uji t permeabilitas tanah kebun karet dan kelapa sawit kedalaman 0-30 cm dan 30-60 cm berbeda tidak nyata. Kemantapan agregat tanah lebih tinggi pada kebun kelapa sawit dibandingkan hutan karet, baik kedalaman 0-30 cm maupun 30-60 cm. Bahan organik pada kebun karet dan kelapa sawit tergolong rendah, baik pada kedalaman 0-30 cm maupun 30-60 cm, N-total rendah, dan C/N rasio rendah serta reaksi tanah (pH) masamKata kunci : Ultisols, Sifat Fisika Tanah, Kebun Karet dan Kelapa Sawit.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".