A holistic solution for the analysis of excavation and specialist data in a 3D GIS framework
Bibliographic record
Abstract
This paper seeks to summarise the development and showcase the uses of a 3D GIS-based tool used by the Keros Project in our work at the Early Bronze Age site of Dhaskalio, Keros. While reflexivity and born-digital tools were built into the design of the 2016-2018 excavations, the issue of how appropriately to use, analyse and make accessible the vast quantities of digital data, especially the over one thousand 3D SFM photogrammetric models, came to the forefront of the post-excavation programme. This tool aims to be a ‘one-stop shop’ for the interpretation of the excavation, including multi-layered 3D views of the site and all geo-located data resulting both from the excavation and from subsequent specialist studies. This is a true 3D GIS system, encapsulating all the abilities of a traditional GIS, including data entry, database management, data analysis and manipulation, while giving access to all excavation and specialist data within a single platform, making the tool data- driven and research-oriented.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".