Identifikasi Sumber Daya Alam yang Unggul di Kabupaten Kayong Utara
Bibliographic record
Abstract
Abstract. The problems of Kayong Utara Regency include the diversity of available natural resources resulting in less focused development activities, so that added value efforts are made to all natural resources that are not superior commodities. This results in the need for identification efforts for all natural resource commodities so that development efforts can be prioritized to achieve maximum results. In addition, development activities for natural resource production results still use traditional methods so that they are still less effective and efficient for use today. This is the basis for the objectives of this study, namely to identify superior natural resources in Kayong Utara Regency. The analysis methods used include Location Quotient (LQ) analysis, Shift Share, and Klassen Typology. The results of the study indicate that Kayong Utara Regency has natural resource potential that can be optimized, including the agricultural sector, especially chili vegetables 16.24%, kencur 3.19%, turmeric 3.13%, duku/langsat 2.24%, large oranges 1, 38%, mango 1.61%, salak 1.70%, coconut 1.23%, marine fisheries 1.09%, goats 3.10%, and cattle 1.14%, with an overall percentage of natural resource calculations that the leading commodities are 20.4%. Abstrak. Permasalahan Kabupaten Kayong Utara diantaranya keberagaman sumber daya alam yang tersedia mengakibatkan kegiatan pengembangan yang kurang fokus, sehingga upaya added value dilakukan kepada semua sumber daya alam yang bukan komoditas unggulan. Hal ini mengakibatkan perlunya upaya identifikasi terhadap semua komoditas sumber daya alam agar upaya pengembangan dapat di prioritaskan untuk mencapai hasil yang maksimal. Selain itu kegiatan pengembangan terhadap hasil produksi sumber daya alam masih mengunakan cara tradisional sehingga masih kurang efektif dan efisien untuk digunakan pada masa sekarang. Hal tersbut yang mendasari tujuan dalam penelitian ini yaitu mengidentifikasi sumber daya alam unggulan di Kabupaten Kayong Utara. Metode analisis yang digunakan meliputi analisis Location Quotient (LQ), Shift Share, dan Tipologi Klassen. Hasil penelitian menunjukkan bahwa Kabupaten Kayong Utara memiliki potensi SDA yang dapat dioptimalkan, meliputi sektor pertanian khususnya sayuran cabe 16,24 %, kencur 3,19 %, kunyit 3,13 %, duku/langsat 2,24 %, jeruk besar 1, 38 %, mangga 1,61 %, salak 1,70 %, kelapa 1,23 %, perikanan laut 1,09 %, kambing 3,10 %, dan sapi 1,14 %, dengan presentase secara keseluruhan perhitungan SDA bahwa komoditas unggulan sebesar 20,4 %.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".