Prarencana Pabrik Minyak Canola \ndengan metode Cold Pressed
Bibliographic record
Abstract
Perkembangan produksi minyak Canola di Canada, Jepang' Amerika, \nAustralia dan beberapa negara yang lain telah membuktikan bahwa Canola telah \nmenjadi salah satu usaha pangan dunia. Canola sebagai tanaman penghasil minyak \nberprospek untuk dapat bersaing dengan edible oil yang lain karena meningkatnya \npermintaan pasar akan minyak goreng yang arnan untuk dikomumsi yaitu memiliki \nkadar asam crucic dan glukosinolat yang rendah akan mendukung perkembangan \nminyak goreng yang berbahan dasar biji canola. \nMinyak canola dapat digunakan sebagai salad dressing, bahan bakar dan \nmargarin. Ada 3 tahapan proses dalam memproduksi minyak canola, yaitu tahap \npersiapan tahap pengepressan dan tahap pemurnian. Pabrik minyak canola \nmenghasilkan limbah berupa gum, sabun dan ampas biji canola yang dapat dijual. \n Perencanaan pabrik minyak canola adalah sebagai berikut : \nJenis proses : cold pressed \nOperasi : kontinu \nProduk : minyak goreng canola \nBahan baliu : biji canola (imporf 4800 ton/tahun \n \nUtilitas : Air = 35,0064 m³/hari \n Bahanbakar :=1981,145L8/ bulan \n Steam = l9l4,52lb/jam \n NaOh: = l4,2kg/hari \nBleaching earth : 32,0688k g/hari \nAir pendingin : 22,4794mᶾ/hari \nJumlah tenaga kerja : 100 orang \nLokasi pabrik : Pandaan Pasuruan- Jawa Timur \nLuas tanah : 5.500 m² \n \nAnalisa ekonomi : \nModal tetap (FCI) : Rp. 35.239.3M.641,62 \nModal kerja (WCI) : Rp. 5.285.895.696,24 \nBiaya Produksi Total (TPC) : Rp. 82.306.097.391,16 \nPenjualan per tahun : Rp.34.713.5261.85, 00 \nRugi per tahun : Rlp.47.592.570.837,16
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.015 |
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".