Bibliographic record
Abstract
Bretez is a cooperative science project linking the humanities and the engineering sciences (digital humanities) whose prime purpose is museography. Its objective, the patrimonial valorisation through its three-dimensional digital restoration and spatial sound restoration, sets itself apart with its new approach to the restoration of the past by combining the 3D with a prominent acoustic aspect that makes the past available and tangible for a very wide audience. This presentation of my work (research focus: archeology of soundscapes) draws on the model that acts as a research template and focuses more specifically on the rendition of soundscapes, while proposing the following question: how can we interpret the past in order to delight our senses without misrepresenting History? Résumé Le projet Bretez est un projet de coopération scientifique associant les sciences humaines et les sciences de l’ingénieur (humanités numériques) dont la destination première est muséographique. Son objectif, la valorisation patrimoniale par sa restitution numérique tridimensionnelle et sonore spatialisée, se dénote par une nouvelle approche de la restitution du passé en combinant la 3D avec une très forte dimension sonore qui rend le passé disponible et tangible pour un très large public. La présentation de mes travaux (axe de recherche: archéologie du paysage sonore) s’appuie sur la maquette qui sert de matrice à la recherche et s’attardera plus particulièrement sur le rendu des ambiances sonores, en proposant cette réflexion: comment ouïr le passé pour réjouir nos sens sans trahir l’Histoire? Mots-clés: Ambiances sonores; archéologie du paysage sonore; humanités numériques
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".