Remote sensing of lichens with drones for detecting dinosaur bones
Bibliographic record
Abstract
Advances in palaeontology and evolutionary biology are often linked to the discovery of new fossils, yet these discoveries are typically serendipitous 1 . Here, we report that lichens can serve as biological indicators of vertebrate fossils in western North America and can be identified using remote sensing. Lichens are symbioses between fungi and algae (and/or cyanobacteria) that play important ecological roles 2 and colonise many substrates, including fossils 3 . Preferential colonisation of dinosaur bones by lichen with vibrant orange pigmentation (Figure 1A,B) has been recognised anecdotally for decades (Darren H. Tanke, personal communication). We found that the spectral reflectance profiles of these lichen pigments and the preferential association between modern lichens and ancient bones can be used to detect dinosaur fossils by remote sensing, for which we propose new spectral indices.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".