Bibliographic record
Abstract
This issue of Internet Archaeology begins with a paper from Dawson and Levy at the University of Calgary in Canada. They combine archaeology and anthropology with 3 dimensional computer reconstructions to view semi-subterranean winter houses from the arctic during the 12th and 13th centuries. The archaeological tradition for the peoples behind these buildings is known as Thule. The Thule have used whale bone extensively in the construction of their houses. Dawson and Levy use reconstructions and ethnographic observation to suggest the ritual and symbolism of the Thule architecture. The rest of the edition is a special issue dedicated to the ARENA (Archaeological Records of Europe: Networked Access) project. ARENA was a three year project supported by the European Union through the Culture 2000 programme. It began in late 2000 and finished in December 2001 and was completed in November 2004. The object of the project was to promote preservation and access to digital data in European archaeology and to investigate and demonstrate the possibility of creating a European information infrastructure for archaeology. Three years of work has covered many areas of interest for the cultural heritage management community and archaeology as a discipline. The set of seven papers presented here can be read in their own right, each taking a separate and vital topic, or they can be seen as a collection. As a collection of papers, it highlights the many difficulties that face us as we seek to find pathways across national boundaries, but they also show us the routes that these pathways can take towards sharing archaeological data across Europe and beyond.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.196 | 0.127 |
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".