Lidar and Lost Cities: Examining the Public Presentation of Recent Lidar Findings through News Media
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.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".