Bibliographic record
Abstract
RESUME. Dans un contexte de conception de produits, les industriels sont conduits a concevoir et realiser une grande diversite de produits pour repondre a des besoins clients et des marches differents. Deux questions couplees apparaissent immediatement qui concernent d’une part la diversite qu’il est necessaire de proposer, et d’autre part la maniere de gerer et produire cette diversite dans des delais et des couts acceptables. La contribution de cet article porte sur la proposition d’une methodologie de conception de produits a forte diversite. Celle-ci s’appuie sur une separation entre les differents types de diversite necessaires a la description du cycle de mise sur le marche d’une famille de produits, et sur la declinaison d’un type de diversite a l’autre en s’appuyant sur les outils disponibles dans la litterature. La proposition de demarche formalisee porte notamment sur l’analyse des besoins fonctionnels (avec une distinction entre les fonctions stables et les fonctions variables), la creation d’une structure fonctionnelle, la creation d’une structure technique et l’analyse de l’ensemble des process utilisables.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".