Bibliographic record
Abstract
Palliser furniture is a Winnipeg-based company that manufactures different types of upholstered products available in Mexico, United States, and Canada. One of Palliser’s popular upholstery that can be found in many theaters today is their Encore Cinema Seats. A problem that all Palliser’s theater clients face daily is the cleaning behind and underneath the seats. The current process for cleaning underneath recliners is ineffective and difficult to perform because the chair itself is in the way during cleaning. Palliser’s customers have reported that the annual cost of cleaning is about half a million dollars. In this project, the team is tasked by Palliser to design a system that will improve the current cleaning process. The team set a goal to improve the cleaning process which will bring the reported annual cost down 25%, decrease cleaning time by at least 50%, and make the cleaning process more ergonomic. The team created a design space to begin generating concepts that will solve the problem. A screening was done on the design space to eliminate incompatible alternatives with the design constraints. The screening gave us ideas on how the recliners can possibly be moved in order to expose the space underneath for cleaning. Design concepts created by the team were narrowed down into four using a Weighted Decision Matrix (WDM). A discussion with the client deemed the four concepts unfit to solve the problem due to the components of the designs being located mainly behind the seats. Seven new concepts were created by the team and further discussion with the client resulted with the final concept the team is going to move forward with. The main components of the final design are the DC motor, rack, pinion, seat bracket, rollers, and roller slots. These components are used in the theatrical seat moving system which will move the recliners forward in order to expose the floor underneath for cleaning. The theatrical seat moving system uses the power output of a DC motor to move the recliners forward. The rotational motion of the motor shaft is translated into linear motion using a rack and [...]
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.130 | 0.042 |
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".