Optimizing Horticultur Commodity-Based Spatial Interactions in West Timor, Indonesia
Bibliographic record
Abstract
This study aimed to analyze: (1) the causes of spatial interaction between the regencies in West Timor based on horticultural commodities, (2) the existing and optimizing benefits of spatial interactions between the regencies in West Timor based on horticultural commodities.The research was conducted in 5 regencies on Timor Island namely Kupang, TTS, TTU, Belu, and Malaka at wholesale markets which were determined purposive and snowball for traders.Purposive sampling with the criteria of traders who market horticultural products to 4 other districts in West Timor, followed by a snowball to trace traders who provide the intended horticultural products to the destination market.Data analysis uses gravity analysis, and the shortest path multi-object optimization model.The results showed that the variables of cost, population, price, and GDP had a significant effect on spatial interactions; while the transport capacity had no significant effect on spatial interactions.The total benefit of spatial interaction between the regencies in West Timor is IDR 1,055,467,000.-.The benefit of spatial interaction will increase by 65.60% to IDR 1,747,888,918,000.-.Therefore, interactions between regions must pay attention to the number of requests, selling prices, and transportation costs in order to maximize benefits.These findings are useful for policy makers and horticultural traders in determining the number of horticultural commodities marketed at each destination market in 4 districts in West Timor that provide optimum profits.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".