Penyebab Menurunnya Kinerja Mesin Pendingin di MV. Vancouver
Bibliographic record
Abstract
A cooling machine or commonly called an Air Conditioner (AC) is a system of auxiliary machines that can create cool temperatures in certain rooms so that workers on ships feel comfortable. Cooling machines are used to regulate and manage air quality which are includes air circulation, air conditioning, air hygiene standards and air purification. There are several main components in the cooling machine, namely the Compressor, Condenser, Expansion Valve, Evaporator. Each of these important components has different functions and ways of operating, but are connected to each other so that the cooling machine can function. The purpose of the author making this final project is to determine the performance of the cooling system, factors that reduce the performance of the cooling machine, as well as repair and maintenance efforts so that the cooling machine can return to normal operation. This research was conducted on the MV. Vancouver by using a qualitative approach. The author conducted direct observations as well as in-depth interviews and documentation studies when conducting research. The results of the author's research found that the main factor causing the decline in the performance of the cooling machine was the appearance of sparks on the evaporator and coil pipe caused by interference with the filter dryer so that it inhibits heat absorption and the evaporator cannot evaporate the refrigerant optimally. The impact caused by the less than optimal operation of the cooling machine in the Engine Room Workshop MV. Vancouver was an unstable room temperature making the crew feel uncomfortable while working or resting.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.123 | 0.027 |
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".