DNA sequencing, microbial sensors, and the discovery of buried mineral resources
Bibliographic record
Abstract
New mineral resources are critical to both sustaining human population growth and technological improvements that will enable global decarbonization. New and innovative exploration technologies that enable detection of deeply buried mineralization and host rocks are required to meet these demands. Here we show that DNA amplicon sequencing of soil microbial communities resolves anomalies in microbial community composition and structure that reflect the surface expression of kimberlite ore bodies buried under 10s of meters of overburden. Indicator species derived from laboratory amendment experiments were employed in an exploration survey in which the species distributions effectively delineated the surface expression of buried kimberlites. Additional indicator species derived from field observations improved the blind discovery of kimberlites buried beneath similar overburden types. Application of DNA sequence-based analyses of soil microbial communities to mineral deposit exploration provides a powerful illustration of how the sensing capabilities of environmental microbial communities can be leveraged in the discovery of critical new resources.
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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 | 0.001 |
| 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".