Debitage and Drones: Classifying and Characterising Neolithic Stone Tool Production in the Shetland Islands Using High Resolution Unmanned Aerial Vehicle Imagery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The application of high-resolution imagery from unmanned aerial vehicles (UAV) to classify the spatial extent and morphological character of ground and polished stone tool production at quarry sites in the Shetland Islands is explored in this paper. These sites are manifest as dense concentrations of felsite and artefacts clearly visible on the surface of the landscape. Supervised classification techniques are applied to map material extents in detail, while a topological analysis of surface rugosity derived from an image-based modelling (IBM) generated high-resolution elevation model is used to remotely assess the size and morphology of the material. While the approach is unable to directly characterize felsite as debitage, it successfully captured size and morphology, key indicators of archaeological activity. It is proposed that the classification of red, green and blue (RGB) imagery and rugosity analysis derived from IBM from UAV collected photographs can remotely provide data on stone quarrying processes and can act as an invaluable decision support tool for more detailed targeted field characterisation, especially on large sites where material is spread over wide areas. It is suggested that while often available, approaches like this are largely under-utilized, and there is considerable added value to be gained from a more in-depth study of UAV imagery and derived datasets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it