Rediscovering the Power of Physical Relief Models: Mayson’s Ordnance Model of the Lake District
Bibliographic record
Abstract
Mayson’s Ordnance model of the Lake District, commissioned by Henry and Thomas Mayson from the sculptor Raffaelle Monti in 1875, was based upon early maps from Ordnance Survey, the government mapping agency for Great Britain. It was displayed in the town of Keswick, now in Cumbria, England, until around 1980, when it is believed to have been destroyed. A large number of original negative moulds from the model were recovered, together with other historical objects, allowing the unique characteristics of the model to be explored for the first time. The study reveals the model to have been innovative for its time, being a very early example of a relief model constructed from contours. The scientific authority of the model, along with its cartographic detail and size, was used to promote it as a spectacle for early tourists. The article describes a process of digital capture, processing, and 3D fabrication that allowed parts of the model to be analysed and redisplayed. An exhibition explored public engagement with physical landscape models, included a novel visitor-led identification of the remaining moulds. Examples of modern landscape modelling and visualization techniques helped to explore the role physical models could play in the modern visitor experience.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".