Bibliographic record
Abstract
This thesis analyzes BTPX 305, a biotech separator developed by Alfa Laval. It focuses on a problem found in the vertical drive where the machine's vibration absorber and damper take place. Today the machine is experiencing vibration above the normal levels, and the product that the machine usually separates is very sensitive to vibration. Therefore, the damping mechanism in the machine needs to be serviced often because of wear in the damping mechanism. In the report, a variety of methods are used, TIPS (the Theory of Inventive Problem Solving) for example are used to model and describe the problem in more detail, and internal/external research is done to possibly find inspiration from other machines that have solutions to similar problems. Solution ideas were generated in brainstorming sessions that were mostly inspired by the information gathered from both the internal and external sources. The concepts were then adapted to then become a solution proposal to the problem. With the help of a priority matrix, it was decided which of the concepts should be considered reasonable as a solution proposal. A couple of experiments were conducted on the concepts that were further taken from the priority matrix to ensure that the theory works in practice.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.031 |
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".