Virtual impact: visualizing the potential effects of cosmic impact in human history
Bibliographic record
Abstract
Current models indicate that catastrophic impacts by asteroids and comets capable of killing more than one quarter of Earth's human population have occurred on average once every million years; smaller impacts, such the 1908 Tunguska impact that leveled more than 2,000 square km of Siberian forest, occur every 200-300 years. Therefore, cosmic impact likely significantly affected hominine evolution and conceivably played a role in Holocene period human culture history. Regrettably, few archaeologists are trained to appreciate the nature and potential effects of cosmic impact. We have developed a conceptual model for an extensible set of educational and research tools based on virtual reality collaborative environments to engage archaeologists and the general public on the topic of the role of cosmic impact in human history. Our initial focus is on two documented asteroid impacts in Argentina during the period of 4000 to 1000 B.C. Campo del Cicio resulted in an energy release of around 2-3 megatons (100-150 times the Hiroshima atomic weapon), and left several craters and a strewn field covering 493 km{sup 2} in northeastern Argentina. Rio Cuarto was likely more than 1000 megatons and may have devastated an area greater than 50,000 km{sup 2} in central Argentina. We are focusing on reconstructions of these events and their potential effects on contemporary hunter and gatherers. Our vinual reality tools also introduce interactive variables (e.g., impactor physical properties, climate, vegetation, topography, and social complexity) to allow researchers and students to better investigate and evaluate the factors that significantly influence cosmic impact effects.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".