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
In this issue, we have grouped together five papers that deal with methods and algorithms applied in cultural heritage.Most of the papers presented in this issue are interdisciplinary as their goal is to answer needs in archeology and simulate virtual museums.The first paper deals with an automated pottery archival and reconstruction system and is written by Martin Kampel and Robert Sablatnig from the University of Vienna.The authors show an interesting application of an automated archival system for archaeological classification and reconstruction of ceramics.The second rather long paper comes from a group of researchers at the National Research Council of Canada that work on 3D imaging technology for museum and heritage applications.The National Research Council of Canada has developed over many years a program of research that has been put in application in museums and cultural agencies.The paper presents a summary of the 3D technology they have developed.This research has led to three patents.Hyun Yang, Taewoo Han and Juho Lee, from KAIST in Korea present a method for reconstructing dynamic 3D scene based on visual hull and view morphing.This method processes multiple synchronized video sequences and generates 3D rendering of dynamic objects in the video.The next paper presents research developed during several years at the University of Florence in Italy.This research has led to a specific software named ArtShop that allows the restoration of artistic images.The last paper is written by three artists, Geeske Bakker, Frans Meulenberg and Jan de Rode who try to identify some problems involved in the reconstruction of historical sites.They mention particularly two problems: the interpretation of data and their diffusion.This paper is more the opinion of artists dealing with virtual heritage than a technical contribution.It raises important questions that researchers should take into consideration.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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