Geospatial Data on Parade: The Results and Implications of the GIS Analysis of Remote Sensing and Archaeological Excavation Data at Fort York’s Central Parade Ground
Bibliographic record
Abstract
This article presents a case study on the application of geographical information systems (GIS) in the context of military archaeology at the Fort York National Historic Site (AjGu-26) in Toronto, Ontario. By employing GIS to amalgamate data from historic mapping, ground penetrating radar, LiDAR, and 30 years of archaeological investigation, the authors reconstruct the historic landscape at the central parade ground of this national historic site. In doing so, they identify the remains of an early 19th-century vice-regal building that served as the official residence of the lieutenant governors of Upper Canada before the American forces burned it down in 1813—an important event that later provided the justification for the British destruction of the White House. With the successful application of GIS to amalgamate multiple lines of evidence, the article serves as another case for the broader acceptance of digital data technologies into the standard methodological toolkits of archaeologists.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".