Application of drone‐captured thermal imagery in aiding in the recovery of meteorites within a snow‐covered strewn field
Bibliographic record
Abstract
Abstract The rapid recovery of meteorites mitigates the exposure of astromaterials to the terrestrial environment and subsequent contamination. Modern fireball observatories have enabled the more accurate triangulation of fireball trajectories, which has aided in the location of strewn fields, in the case of meteorite‐producing events. Despite this advancement, most meteorite searches still use manual searching to locate any meteorite falls, which is often labor‐intensive and has a slow coverage rate (km2 day−1). Recent work has begun exploring the application of drone technology to the recovery of meteorites; however, most of this work has focused on falls in arid environments. Our study examines the utilization of drones with thermal imaging technology to aid in the recovery of meteorites that have fallen on a snow‐covered field. We created a simulated strewn field that included meteorite specimens as well as Earth rocks with similar properties (“meteowrongs”). Thermal imagery was utilized to determine whether the thermal contrast between meteorites and snow could aid in the identification of meteorites. We found that the thermal contrast was significant enough that meteorites were readily identifiable within thermal images; however, it was not significant enough to distinguish between the meteorites and the meteowrongs. The utilization of thermal imagery in conjunction with visible imagery has the potential to aid in the rapid recovery of meteorites in snow‐covered landscapes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".