DESIGN HARNESSING HOT SPRING’S ENERGY SYSTEM FOR COCOA BEANS DRYING
Bibliographic record
Abstract
Geothermal energy is a one type source of alternative energy. Countries like United Stated, Australia, New Zealand, China, Canada and Turkey have used this energy for some applications such as for electricity generation, space and district heating, air conditioning, greenhouse heating and others. The presence of volcanoes, hot springs and other thermal phenomena lead people to explore and study energy produced inside earth. In Malaysia, there are also some places have the hot springs in Tambun, Sungkai, Pengkalan Hulu, Manong and others and currently use for recreation and tourism. For this project, the author wants to study and understanding this one type of alternative energy and design suitable system to harness hot spring’s energy and used for cocoa beans drying (industrial application). The problems that are being created from energy price hiking and our equator climate lead the author to find the solutions. The target is to design a suitable system which can harness hot spring’s energy and applied it on cocoa beans drying process. Since the energy is free and continuously produced inside the earth, the author wants to find the way how to commercial the energy. The important elements that need to be determine before starts the design is the behaviour of cocoa beans while drying and the unknowns related to the design that must being determine using the experiment and engineering calculations. The design must be in details for future works. This project can solve the problems of inconsistently weather condition and high price for fuel and gas that being used for artificial drying. This project also can make improvement with maximizing the quantity of the dried cocoa beans without affecting the quality.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".