Pemetaan Komoditas Basis di Kecamatan Polongbangkeng Utara Kabupaten Takalar
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.
Bibliographic record
Abstract
The policy for commodity development in Polongbakeng Utara District has not been able to optimize its natural resource potential. One of the efforts to optimize this potential is the identification of food crops basis commodities by potential mapping in each village in Polongbangkeng Utara District. This study aims to identify the food crops commodity which is a basis commodity and to make the basis commodities mapping in Polongbangkeng Utara District. The analytical method used is LQ analysis and Ar-GIS mapping. The results showed that the food crops commodities which were the basis commodities in Polongbangkeng Utara District were rice, corn, green beans, cassava and sweet potatoes. Palleko is a village that has the most basis commodities with 4 basis commodities, namely rice, green beans, cassava and sweet potato. Rice and sweet potato commodities are the most basis commodities because they are the basis for 12 villages out of 18 villages in Polongbangkeng Utara district. Base commodity mapping was carried out on 5 food crop commodities. Mapping results show that there are more non-basis commodity polygons (50 polygons) than basis commodity polygons (40 polygons).
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it