Condition Recording for the Conservation and Management of Large, Open-Air Sites: A GIS-Based Approach
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
This paper was delivered at the 107th Annual Meeting of the Archaeological Institute of America, Montreal, Canada on January 8, 2006, as part of a panel on conservation in Ukraine organized by ICA. The importance of condition recording has been given particular emphasis in recent years in the discussion of conservation and management planning for archaeological sites. The initial assessment and recording of a site’s current conditions is a fundamental first step in any conservation project. It provides a base-line of information about the material make-up of the site, its current level of deterioration, and the extent and nature of previous interventions, thus enabling intelligent budgeting and planning decisions and identifying priorities for the development of a sensible conservation plan. Unfortunately, however, thorough condition recording can be a daunting, if not impossible, task at large, complex archaeological sites. Systematic and detailed condition surveys are seldom undertaken on large archaeological sites due to the level of human resources required to collect the data and the problems involved in managing and accessing these data in a way that allows meaningful conclusions to be formed. In order to address this challenge at Chersonesos – a large, multi-phase site in Crimea, Ukraine – a GIS-based condition recording system was developed by a joint team from the University of Texas at Austin Institute of Classical Archaeology and the National Preserve of Tauric Chersonesos. This paper presents a detailed overview of the recording system, presents the results of a test season implementing it in the ancient city center of Chersonesos, and discusses its merits as a model for condition recording and long-term monitoring for complex, open-air sites.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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