Bibliographic record
Abstract
Airborne laser scanning or lidar has now been used by archaeologists for twenty years, with many of the first applications relying on data acquired by public agencies seeking to establish baseline elevation maps, mainly in Europe and North America. More recently, several wide-area acquisitions have been designed and commissioned by archaeologists, the most extensive of which cover tropical forest environments in the Americas and Southeast Asia. In these regions, the ability of lidar to map microtopographic relief and reveal anthropogenic traces on the Earth’s surface, even beneath dense vegetation, has been welcomed by many as a transformational breakthrough in our field of research. Nevertheless, applications of the method have attracted a measure of criticism and controversy, and the impact and significance of lidar are still debated. Now that wide-area, high-density laser scanning is becoming a standard part of many archaeologists’ toolkits, it is an opportune moment to reflect on its position in contemporary archaeological practice and to move towards a code of ethics that is vital for scientific research. The papers in this Special Collection draw on experiences with using lidar in archaeological research programs, not only to highlight the new insights that derive from it but also to cast a critical eye on past practices and to assess what challenges and opportunities remain for developing codes of ethics. Using examples from a range of countries and environments, contributions revolve around three key themes: data management and access; the role of stakeholders; and public education. We draw on our collective experiences to propose a range of improvements in how we collect, use, and share lidar data, and we argue that as lidar acquisitions mature we are well positioned to produce ethical, impactful, and reproducible research using the technique.
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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 | 0.054 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.088 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".