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STRATEGI PENGEMBANGAN KAWASAN AGROPOLITAN BERBASIS TANAMAN PANGAN DI KOTA PADANG (Agropolitan Development Strategy Based on Food Crops in Padang City)

2014· article· en· W2335395591 on OpenAlex

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

VenueJurnal Tataloka · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Economic Development and Planning
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsAgricultureBusinessAgribusinessAgricultural economicsCommodityAgricultural scienceEnvironmental planningGeographyEconomicsEnvironmental scienceFinance

Abstract

fetched live from OpenAlex

The role of Agricultural sector isn't only to ensure food supplies but also as an income generating through export activities. Padang is potentially developed to modern agriculture due to vast number of agriculture land. Base on regulation No.18 of 2004 on RPJP and Regulation No.9 of 2009 on RPJM, Padang is declared as Agropolitan area. The Agropolitan concept is expected to increase agriculture-based development, because the GDP share of agriculture is relatively small. This study aims are to analyze a basic food crops, 2) to choose the region which will develop as agropolitan area base on availability of facilities and infrastructure in that region and 3) to formulate development strategies of agropolitan area. The analysis of LQ indicate rice crop as the basic commodity with the LQ greater than one. Skalogram analysis and diamond porter show that Sub District Kuranji is potentially developed as a growth center in the Agropolitan Area because it has the most complete facilities and infrastructure. In this research the development strategies are to develop agricultural infrastructure (sub terminal of agribusiness) and institutional strengthening of capital institution.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.278
Teacher spread0.241 · 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