A methodological approach to rock art survey and recording via drone. The application to the Rock Art of the Mediterranean Basin of the Iberian Peninsula assemblage
Bibliographic record
Abstract
The significant advancement in drone technology has led to increased usage across different scientific domains. In the field of archaeology, drones became increasingly popular a decade ago, primarily for photogrammetric documentation or aerial photography. Since then, researchers have experimented with new applications, notably utilizing LiDAR imagery to enhance archaeological surveying. In this context, one of the latest applications involves surveying open-air rock art shelters in inaccessible locations to search for prehistoric rock art imagery. The current study involves refining the methodology used for this purpose in the territory of UNESCO’s World Heritage List property Rock Art of the Mediterranean Basin of the Iberian Peninsula, utilizing a DJI Mavic 3 drone, which represents a significant improvement over previous models. On the other hand, it highlights the potential for its utilization in conservation studies and managing human activity in their environments, considering the threats to which these sites are currently exposed.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".