Digital Vacone
Bibliographic record
Abstract
Between 2016 and 2018, excavations at the Roman villa of Vacone, carried out by the Upper Sabina Tiberina Project, transitioned to completely digital recording practices. The methodological shift was accompanied by a three-year campaign of backfill removal and cleaning, which allowed most of the villa’s extant architecture and décor uncovered since 2012 to be digitized. Moreover, a new documentation protocol was established that employs photogrammetry in lieu of scale drawing to model the three-dimensional spatial characteristics of every archaeological context. Notable artifacts were also modeled to facilitate off-site study. The excavation’s experiences with this conversion offer valuable lessons for other long-term archaeological projects contemplating a similar shift amid active fieldwork. The project’s digital recording team developed a methodology for layer-by-layer modeling that ensures precise alignment between stratigraphic contexts using fixed markers. From these, standard 2D products (orthomosaic plans and digital elevation models [DEMs]) were produced. A similar technique was used for generating 2D orthomosaics of vertical features (such as walls and stratigraphic sections) without the need to take numerous measurements on the vertical surface (e.g., with a prismless total station). Similarly, the generated data can create 2D sections along any arbitrary line even after the strata have been removed. Beyond simply replicating traditional two-dimensional records, the 3D data have proven essential for visualizing the interrelation of above and below ground spaces, and for analyzing a terraced structure built on several levels. Composite 3D models, hosted online, are also an effective tool for public outreach with stakeholders in the local community, as well as 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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.659 | 0.389 |
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".