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Record W4392883381 · doi:10.33512/jat.v16i2.23205

Nilai Ekonomi Lahan Pertanian di Wilayah Kerja Cabang Dinas Kehutanan IV Kabupaten Cianjur Jawa Barat

2023· article· id· W4392883381 on OpenAlexaff
Miftakhul Arifah, Andjar Astuti, Aris Supriyo, Sulaeni Sulaeni

Bibliographic record

VenueJurnal Agribisnis Terpadu · 2023
Typearticle
Languageid
FieldAgricultural and Biological Sciences
TopicAgriculture and Agroindustry Studies
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPolitical scienceForestryGeography

Abstract

fetched live from OpenAlex

Land is the most important resource in supporting agricultural activities but land is limited. Therefore, land use must be wise and need economic considerations. The economic value of agricultural land is seen based on the level of fertility which is quantified by the production and production costs. This study aims to determine the average economic value of agricultural land and determine the most optimum economic value of land based on the types of commodities in Forestry Service Branch IV Working Area, Cianjur Regency, West Java. This research was conducted in Cianjur Regency, West Java from November 2022 to May 2023. Data was collected through direct interviews with 40 respondents. The average economic value of agricultural land in Forestry Service Branch IV Working Area, Cianjur Regency, West Java is Rp. 8.130/m²/cycle. The most optimal economic value of land in one cycle is the chili commodity in the amount of Rp. 9.530/m²/cycle while the most optimum economic value of land in one year is the cabbage commodity of Rp. 28.218/m²/year.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.002

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 head0.025
GPT teacher head0.227
Teacher spread0.202 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2023
Admission routes1
Has abstractyes

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