Bibliographic record
Abstract
With the growing importance of mapping land, regions, and their related features, Geographic Information Systems (GIS) has become an ever important standard in fields where such detailed study of land features is required. Our archaeology digital library, ETANA-DL (http://etana.dlib.vt.edu), contains thousands of records from eight member excavations. Here, we draw on the Space aspect of the 5S meta-model [1] for digital libraries and demonstrate a methodology used to integrate archaeological GIS data with the wealth of information within ETANA-DL. ETANAGIS connects the digital library's textual records with a spatial representation of their original locations, enhancing users' understanding of the find.Using a dataset of the University of Toronto's Tell Madaba excavation project [2], we developed an interactive, Web-based representation of the original ArcGIS document (accessible from ETANA-DL homepage). For dynamic generation of maps from geospatial data, we use the MapServer [3] project, a mature, project which boasts a rich toolset of features for cartographicrelated image generation. MapServer can directly utilize ArcGIS layer resources but some translation and additional authoring must occur for proper image generation. Then, using PHP, the MapScript MapServer API, and navigation tools, the map was ported to an interactive, Web-accessible format. Based on a study of alternatives, the technology we chose for our technique seemed to be the best suited for digital library integration and is also completely open source.To explore the presentation of the map, a user employs the navigation tools displayed in the corner of the main view (see Figure 1). In addition, full control of displayed layers, a smaller map showing overall view and context, as well as a dynamic scale bar are available for use. To integrate the Web-based version of the Tell Madaba GIS map with the existing digital library, the layers depicting archaeological divisions are clickable and labeled for easy identification. Any area queried results in a pop-up box with ETANA-DL's records and artifacts for that area.While this integration connects the digital library with the spatial representation of the region, the unique quality of various GIS maps causes certain difficulties. The lack of standard in denoting spatial divisions in GIS is one hindrance to producing a more automated technique. Future work will include more automation, usability evaluation, and integration of additional excavations. We hope integration of the digital library and GIS greatly aids users' understanding of the spatial organization of the included data.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".