Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<em>Bretez</em> 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? <strong><br /></strong> <strong>Résumé</strong> Le projet <em>Bretez</em> 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? <strong>Mots-clés:</strong> 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it