Lidar and Lost Cities: Examining the Public Presentation of Recent Lidar Findings through News Media
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
Overview This review considers how scientific archaeological publications, especially those relying on new digital technologies, can become sensationalized for the public in popular media. I present three separate examples of lidar-based mappings of ancient landscapes in the Amazon and Central Asia, each initially published by archaeological teams in the journals Nature or Science since 2022. These academic publications were followed by many news articles in the popular press. A common trope of these popular presentations includes the concept of “lost cities” being finally “found” by the lidar surveys. This oversimplification usually ignores existing knowledge, especially that of Indigenous local communities and archaeologists. We archaeologists should, therefore, become more aware of the potential consequences of our scholarly communications. We should consider the public’s experience with parsing scientific advances and what ways we can try to influence the public discourse.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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