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Record W4412674450 · doi:10.59465/jpht.v16i2.826

Distribusi Tanaman dan Nilai Ekonomi Hutan Kemasyarakatan di Kecamatan Batukliang Utara Kabupaten Lombok Tengah

2019· article· id· W4412674450 on OpenAlexaff
Chairil Anwar Siregar, Alfonsus H. Harianja, Dalilah Dalilah, Sidiq Cahyono, Soraya Ulfah

Bibliographic record

VenueJurnal Penelitian Hutan Tanaman · 2019
Typearticle
Languageid
FieldAgricultural and Biological Sciences
TopicAgriculture and Agroindustry Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsForestryGeography

Abstract

fetched live from OpenAlex

Pembangunan Hutan Kemasyarakatan (HKm) di Kabupaten Lombok Tengah telah dimulai sejak tahun 1999 dalam bentuk ijin sementara pengelolaan HKm dan kemudian diterbitkan Ijin Usaha Pengelolaan Hutan Kemasyarakatan (IUP-HKm) pada tahun 2010 dengan luas areal 1.809,5 ha. Kawasan HKm dalam waktu tiga belas tahun (2000-2013) telah membentuk formasi hutan pola agroforestri. Namun demikian, distribusi tanaman dan nilai produksi kawasan belum diketahui. Untuk itu dilaksanakan penelitian guna mengetahui komposisi tanaman dan nilai produksinya. Penelitian dilaksanakan menggunakan metode survei dengan melakukan analisa vegetasi untuk mengetahui distribusi tanaman dengan intensitas sampling sebesar 0,01% atau 1,81 ha dengan jumlah plot sebanyak 45 unit. Untuk mengetahui nilai ekonomi dari produksi tanaman dalam HKm, dilaksanakan wawancara terhadap petani penggarap HKm dengan intensitas sampling 1% atau sebanyak 32 petani. Hasil penelitian menunjukkan bahwa komposisi tanaman yang ada dalam areal HKm terdiri dari strata pohon sebesar 2,02%, tiang 4,12%, pancang 26,44% dan semai 67,4%. Kerapatan tanaman 11.462 btg/ ha yang didominasi oleh tanaman kopi (Coffea sp.), pisang (Musa sp.), durian (Durio zibethinus), coklat (Theobroma cacao) dan nangka (Arthocarpus heterophyllus) dengan proporsi berturut-turut 24,08%; 13,70%; 9,25%; 7,48% dan 5,30%. Nilai ekonomi yang diperoleh rumah tangga petani rata-rata sebesar Rp 6.366.484/tahun atau Rp 530.540/bulan. Terdapat kecenderungan penurunan nilai produksi HKm akibat semakin meningkatnya penutupan lahan. Untuk meningkatkan nilai tukar komoditas HKm, diperlukan program strategis yang dapat mendorong pembangunan HKm.

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)0.000
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), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.015
GPT teacher head0.210
Teacher spread0.195 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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