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Record W4381621745 · doi:10.31315/psb.v4i1.8886

Evaluasi Lahan Berdasarkan Kualitas dan Karakteristik Lahan pada Bekas Pertambangan Tanah Urug di Dusun Pucang Gading, Kelurahan Hargomulyo, Kapanewon Kokap, Kabupaten Kulon Progo, Daerah Istimewa Yogyakarta

2023· article· id· W4381621745 on OpenAlexaff
Wais Alfajri, Wisnu Aji Dwi Kristanto, Dian Hudawan Santoso

Bibliographic record

VenueProsiding Seminar Nasional Teknik Lingkungan Kebumian SATU BUMI · 2023
Typearticle
Languageid
FieldEnvironmental Science
TopicSoil and Land Suitability Analysis
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsForestryPhysicsGeography

Abstract

fetched live from OpenAlex

Indonesia memiliki banyak aktivitas pertambangan, salah satunya kegiatan pertambangan tanah urug yang berlangsung diDusun Pucang Gading, Kelurahan Hargomulyo, Kapanewon Kokap, Kabupaten Kulon Progo, Daerah Istimewa Yogyakarta.Aktivitas pertambangan membuat lahan menjadi terdegradasi. Tujuan dari penelitian ini yaitu mengetahui kualitas dankarakteristik lahan berdasarkan kesesuaian lahan untuk arahan teknis reklamasi pertambangan sebagai pertanian lahan keringtanaman sengon dan ketela pohon. Metode yang digunakan adalah (1) survei dan pemetaan (2) Purposive Sampling (3)analisis laboratorium (4) weight factor matching. Parameter (karakteristik lahan) yang diamati pada lapangan yaitutemperatur(t) (rerata temperatur tahunan), ketersediaan air(w) (bulan kering, hujan pertahun), media perkaraan(r) (drainasetanah, tekstur tanah dan kedalaman efektif), retensi hara(f) (PH, H2O, KTK tanah, C-Organik), hara tersedia(n) (N Total, P2O5,K2O5), penyiapan lahan(p) (batuan permukaan, singkapan batuan), tingkat bahaya erosi(e) (bahaya erosi, lereng). Berdasarkanhasil dari evaluasi kesesuaian lahan tanaman sengon didapatkan 3 kelas, sedangkan tanaman ketela pohon didapatkan 2kelas. Rekayasa yang dilakukan untuk memperbaiki lahan adalah rekayasa teknik dengan pembuatan teras jenjang,pembuatan saluran irigasi dan revegetasi. Upaya perbaikan lahan yang dilakukan diharapkan membuat lahan kembalimenjadi produktif.Kata Kunci: Kualitas lahan; Karakteristik Lahan; Evaluasi Lahan

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)0.005
metaresearch head score (Gemma)0.002
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 score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.004
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.003

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 head0.025
GPT teacher head0.273
Teacher spread0.249 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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