La densité : concept, exemples et mesures : éclairage sur le concept de densité et sur les différents usages de ses mesures
Bibliographic record
Abstract
Un seuil de peuplement, un sentiment d’entassement, des données démographiques, un coefficient d’occupation des sols, l’étalement urbain… On peut voir que le concept de densité a renvoyé à différentes dimensions au cours de l’histoire.La densité est une donnée sans signification intrinsèque. Elle n’a de pertinence que pour comparer des territoires entre eux ou dans le temps. Cet instrument de mesure est le rapport d’éléments dénombrables sur une surface donnée. Il ne prend son sens qu’en fonction de l’objet d’étude (formes urbaines, fonctions urbaines, activités, mobilité, environnement végétal…) et de l’échelle de l’analyse (îlot, quartier, agglomération, aire urbaine…). Il est donc important de s’entendre sur la terminologie employée dans chaque cas et sur le mode de calcul utilisé, notamment sur la prise en compte des vides urbains et de l’échelle d’application préférentielle.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".