DOCUMENTING NEA PAPHOS FOR CONSERVATION AND MANAGEMENT
Bibliographic record
Abstract
Abstract. A cornerstone of the management and conservation of archaeological sites is recording their physical characteristics. Documenting and describing the site is an essential step that allows for delineating the components of the site and for collecting and synthesizing information and documentation (Demas, 2012). The information produced by such work assists in the decision-making process for custodians, site managers, public officials, conservators, and other related experts. Rigorous documentation may also serve a broader purpose: over time, it becomes the primary archival and monitoring record. Both scholars and the public use this information and interpret the site, and they can serve as a posterity record in the event of catastrophic or gradual loss of the heritage asset. In May 2018 the Getty Conservation Institute and the Department of Antiquities of Cyprus collaborated with the Carleton Immersive Media Studio in undertaking the documentation of Nea Paphos, a World Heritage site with very important mosaic pavements in the eastern Mediterranean. This contribution outlines the critical components of the documentation project: field study, field measurements, data processing, validation, GIS, and integration of external data. The paper summarizes the digital workflows and procedures used to produce the deliverables, as well as the equipment and technology employed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".