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OPTIMALISASI PENGEMBANGAN KAWASAN AGROPOLITAN MALALO MELALUI PEMETAAN KOMODITAS UNGGULAN

2021· article· id· W4205556464 on OpenAlex
Muhammad Anshar, Irsyadi Siradjuddin, Amirin Kusmiran

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTEKNOSAINS MEDIA INFORMASI SAINS DAN TEKNOLOGI · 2021
Typearticle
Languageid
FieldAgricultural and Biological Sciences
TopicAgriculture and Agroindustry Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsAgricultural scienceBusinessBiology

Abstract

fetched live from OpenAlex

Pengembangan kawasan agropolitan Malalo saat ini belum optimal karena perencanaan pengembangan wilayah berbasis potensi komoditas yang tidak terkait sehingga komoditas unggulan yang dikembangkan tidak tepat sasaran. Oleh karena itu, komoditas pertanian yang menjadi komoditas basis yang dijadikan komoditas unggulan perlu dikenali. Pemetaan komoditas dikawasan agropolitan Malolo dilakukan dengan menggunakan software ArcGIS dan dianalisis secara deskriptif kualitatif dan kuantitatif. Hasil penelitian menunjukkan bahwa komoditas pertanian yang menjadi komoditas pokok di kawasan agropolitan Malolo adalah padi, jagung, kacang hijau, ubi kayu, dan ubi jalar. Dari komoditas tersebut, komoditas unggulan yang dapat dijadikan komoditas unggulan di kawasan agropolitan Malolo adalah komoditas jagung yang tersebar di desa-desa dan keluarga, yaitu Towata, Barugaya, Timbuseng, dan Massamaturu.

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.

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)0.002
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), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.031
GPT teacher head0.234
Teacher spread0.203 · 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