Study of Leading Products to Map the Potential of Agribusiness For Tourism Development in the City of Banjarmasin
Bibliographic record
Abstract
The study aims to map the potential of superior agribusiness products for tourism development in Banjarmasin City. The research method is quantitative descriptive using secondary data obtained from BPS South Kalimantan Province and Banjarmasin City. The analysis technique identifies the internal potential of a region, namely basic sectors and non-basic sectors. The potential for agribusiness in the Food Crop Agriculture Sub Sector in Banjarmasin City which can become a base for food crop commodities is the South Banjarmasin District and the North Banjarmasin District. The potential for agribusiness in the Livestock Sub Sector in Banjarmasin City which can be the basis for the livestock sub-sector is the District of South Banjarmasin and Central Banjarmasin. The Fisheries Sub Sector in Banjarmasin City which can be the basis for the livestock sub sector is the North Banjarmasin District and the East Banjarmasin District. The results of research on agribusiness potential in Banjarmasin City seen from the Food Crop Agriculture Sub-Sector include rice, Siamese oranges, and bananas. The agribusiness potential of the Livestock Sub-Sector includes beef cattle, broiler chickens, laying hens, ducks, quail, goats, and free-range chickens. The Fisheries Sub-Sector includes: capture fisheries and freshwater floating net cultivation fisheries, capture fisheries, and still water pond cultivation fisheries.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".