Perspektif Pengembangan Jaringan Transportasi Dalam Mendukung Kek Barru Sulawesi Selatan
Bibliographic record
Abstract
Kabupaten Barru sebagai 'Pusat Titik Tangkap' kornoditi di Sulawesi Selatan. Dengan adanya rencanaKEK Barru diharapkan dapat mendukung pertumbuhan ekonomi di Sulawesi Selatan melaluipengembangan simpul-simpul strategis jaringan transportasi jalan, penyeberangan dan laut. Tujuanpenelitian ini menentukan sektor basis dan komoditas unggulan dari hinterland KEK Barru serta mengetahuikondisi jaringan prasarana transportasi dalam mendukung rencana KEK Barru di Sulawesi Sela tan. Penelitianini menggunakan pendekatan deskriptif kualitatif. Metode analisis adalah Location Quotient (LQ) dan analisisjaringan. Hasil penelitian menunjukkan bahwa sektor basis berupa perikanan (komoditas hasil laut),peternakan (komoditas sapi dan unggas) dan pertanian (padi). Pengembangan jaringan prasaranatransportasi dalam mendukung rencana KEK Barru adalah jaringan transportasi jalan, transportasi lautdan udara. Pelabuhan Makassar sebagai pintu gerbang utama bagi Kawasan Timur Indonesia (KTI), untukmendukung menjadi Greater Port of Makassar, maka Pelabuhan Garongkong yang terletak di KEK Barru ijuga ikut dikembangkan guna mendukung fungsi pelabuhan sebagai pengembangan terminal curah nonpakan. Lokasi KEK Barru yang terletak dekat dengan Bandara Intemasional Sultan Hasanuddin juga sangatmendukung kelancaran akses distribusi komoditi yang tidak dapat dilayani rnelalui transportasi laut.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".