Bibliographic record
Abstract
When purchasing a product, and in particular when many different brands compete in the market, the first impression will consistently affect a customer's choice. When buying a car, the ease with which the doors close, the speed and the sound of the closure must give the customer the impression of a quality car. The recent stringent regulations on fuel economy and pollution have forced car makers to look for new solutions to lower the fuel consumption of the vehicles of their fleet and meet the standards imposed. The most obvioussolutions to reach the target are the improvement in the efficiency of engines and the reduction in vehicle weight. In the recent past, the use of aluminum (mainly in the form of alloy) in the automotive industry has increased due to its lower weight with respect to steel, even if the higher costs remain a big hindrance to the large-scale use of this metal. When dealing with door closing effort, the use of aluminum introduces some issues that relate directly to the lower weight of the door and possibly to other factors dependent on the different materials. The closing performance of the door is therefore expected to change, and the variations in the contributors to the closing effort need to be analyzed and discussed. In this work, the door closing performance of an aluminum door and of a steel door of a C segment vehicle are fully compared to study what changes, and to which extent, in the closing effort, due to the lighter alloy used. Two means are used, the physical testing of the doors in the body shop with the EZ Slam technology, and the simulation of the closing event with an existing closing effort predictive model. Particular focus is put on the contribution of the check system to the closing effort and how its final profile affects the closing event of the door.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".