Karakteristik dan Potensi Mata Air Panas Untuk Pengeringan Biji Kopi Di Candi Gedong Songo, Desa Candi, Kecamatan Bandungan, Kabupaten Semarang, Provinsi Jawa Tengah
Bibliographic record
Abstract
Daerah penelitian memiliki manifestasi panas bumi berupa mata air panas dengan sebagian besar penduduknya melakukan usaha pertanian, termasuk petani kopi. Biji kopi untuk dapat dikonsumsi memerlukan proses pengeringan. Pengeringan menggunakan mata air panas tidak akan menghasilkan emisi dan tidak mengkhawatirkan cuaca. Daerah penelitian memiliki curah hujan yang tinggi. Penelitian dilakukan di Candi Gedong Songo, Desa Candi, Kecamatan Bandungan, Kabupaten Semarang, Provinsi Jawa Tengah. Tujuan penelitian ini adalah untuk mengetahui karakteristik dan potensi mata air untuk pengeringan biji kopi di Desa Candi. Metode yang digunakan dalam penelitian yaitu survei lapangan, uji laboratorium dan analisis kimia. Hasil penelitian menunjukkan suhu permukaan mata air panas 63 oC, pH 2.7 dan debit mata air 0.325 l/s. Daerah penelitian memiliki curah hujan yang tinggi. Tipe mata air panas berdasarkan analisis kimia yaitu air sulfat (SO4). Mata air panas berada pada zona immature water. Perkiraan suhu reservoir menggunakan metode geothermometer yaitu 354 oC masuk ke dalam entalpi tinggi. Mata air panas di daerah penelitian memiliki potensi yang baik untuk dimanfaatkan sebagai pengeringan biji kopi.Kata Kunci: Geothermometer, Manifestasi, Mata Air Panas, Pengeringan, Potensi, Biji kopi, Pengeringan
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".