Bibliographic record
Abstract
Automakers are using French-born manufacturing software to improve the machining and assembly of their vehicles. Carmakers use ILOG software to determine the order of building vehicles that will optimize production, maximizing return on investment. A more recent French software entrant in the Detroit area is ILOG, which opened a sales and technical support office in Southfield, Michigan, in May 2000, to serve the US automotive market. Delmia Corp.’s, a French company, labs in Troy, Paris, Montreal, Stuttgart, and Bangalore, India, customize software services to design, simulate, optimize, and control production activities, which account for up to 80 percent of the cost of manufactured goods. Delmia adapted three of its proprietary software tools to form the core software of the V-Comm Project. The Delmia Assembly Module enables users to evaluate alternative sequences of assembly to achieve the optimal lean solution. Toyota engineers working in V-Comm rooms at 20 Toyota locations in Japan, Europe, and North America use the Delmia software to create virtual prototypes that are projected on large screens, and to observe the visual data in three dimensions.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.549 | 0.252 |
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".