ANALISIS KOMODITAS UNGGULAN DAN ARAHAN RENCANA PENGEMBANGANNYA DI KOTA PAGAR ALAM,PROVINSI SUMATERA SELATAN
Bibliographic record
Abstract
The Development of competitive commodities in a region are expected to improve added value of the commodities, to increase society income and to improve regional economic conditions. This study was conducted (1) to analyse competitive commodities of agriculture in each district, (2) to analyse potential land for competitive commodities development (3) to analyse regional hierarchies (4) to formulate direction of competitive commodities development plan. The competitive commodities were obtained using Location Quotient (LQ) and Shift Share Analysis (SSA). Analysis potential land for competitive commodities development was analysed using land availability and suitability and geographical information system. Regional hierarchy was analysed using schalogram method. Competitive commodities development direction considered based on potential land, regional hierarcy, compactness of land and local government policy. The results showed that competitive commodities in every district were coffee robusta, rice farming and cabbage. The direction development of coffee was given priority in South Dempo district area of 2,824.26 ha. Rice farming was given priority in Central Dempo district area of 1,496.13 ha. Meanwhile, development of cabbage is not available.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".