Applications of geographic information science in the archaeological research of the Fincastle Kill Site (D1Ox 5) Alberta, Canada, and Tel Beth-Shemesh, Israel
Bibliographic record
Abstract
Many scientists have used the expediency of geographic information science (GIS) for archaeological analyses, such as predictive site location modeling and producing topographical site surveys. However, the use of GIS to explore the spatial relationships among the architecture, geography and site artifacts has rarely been done. This research focuses on visualizing and analyzing these relationships using GIS. The sites of Tel Beth Shemesh, Israel and the Fincastle Kill Site (DIOx 5), north-east of Taber, Alberta, were used as case studies, as they were very differnt types of sites. Based on field measurments and by using specific GIS applications and software, components of these sites were reconstructed in virtual space as GIS models. Other recorded field data were used as input parameters into the models in order to attain the most accurate representations and analyses of the sites. The analysis at Fincastle Kill Site used two types of GIS models: 1) a viewshed model to assess possible bison hunting techniques and 2) surface interpolation models that delineated correlations between high density and low density areas of archaeological remains. The investigation at Tel Beth-Shemesh used a GIS model to store, visualize, interpret and assess the quality and accuracy of the field data recorded during 2001 - 2004 excavations. Predominately, the work in this thesis did not aim at answering any profound questions about the archaeology of either site, although in some cases it did, but rather focused on developing useful GIS tools for the archaeologist. These GIS models show the value of the applications, and their applicability to archaeological sites around the world.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".