Theorising 3D Visualisation Systems in Archaeology: Towards more effective design, evaluations and life cycles
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
3D visualization in archaeology has become a suitable solution and effective instrument for the analysis, interpretation and communication of archaeological information. However, only few attempts have been made so far for understanding and evaluating the real impact that 3D imaging has on the discipline under its different forms (offline immersive and not immersive, and online platform). There is a need in archaeology and cultural heritage for a detailed analysis of the different infrastructural options that are available and a precise evaluation of the different impact that they can have in reshaping the discipline. To achieve this, it is important to develop new methodologies that consider the evaluation process as a fundamental and central part for assessing digital infrastructures. This new methods should include flexible evaluation approaches that can be adapted to the infrastructure that need to be assessed. This paper aims at providing some examples of 3D applications in archaeology and cultural heritage and describing how the selection of the infrastructure is related to specific needs of the project. This work will describe the different applications and propose guidelines and protocols for evaluating their impact within academia and the general public.
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 | 0.001 |
| 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 | 0.001 |
| 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