Microorganisms as sensors for concealed mineral deposits ; application and development of microbiological mineral exploration in the Northwest Territories and British Columbia, Canada
Bibliographic record
Abstract
Mineral exploration is becoming more challenging in that undiscovered deposits are likely concealed beneath thick cover sequences. Current, through-cover, geochemical methodologies often provide inconsistent results and have poorly developed anomalies that may go undetected. The development of innovative exploration strategies and robust techniques to see through cover is thus imperative to future discovery success. Profiling of microbial communities that populate the soils above mineral deposits provide a solution for geologists exploring in covered terrains. Microorganisms are well equipped to detect geochemical gradients as they are highly sensitive to subtle differences in the chemical and physical properties of their surroundings. High-throughput DNA sequencing technology and big-data analysis techniques have now advanced to the point that high-resolution information on microbial community composition and structure is readily accessible. My results have demonstrated the viability of microbial fingerprinting to directly identify the surface projection of buried kimberlites and porphyry copper deposits. Lab incubations and case studies from the Northwest Territories and south-central British Columbia were used to test the efficacy of microbial community profiling in deposit-scale exploration. Resulting 16S sequencing-derived datasets were integrated with chemistry, mineralogy, surface and sub-surface geology. My analyses show statistically significant microbial community shifts, correlated with the presence of porphyry copper mineralization and kimberlites, with a distinct community response at the species level directly over known deposits. The diversity of soil bacteria at kimberlites is also depressed in the same regions where microbial community profiles were anomalous. The observed relationship between microbial communities and buried mineralization demonstrates the power of microbial fingerprinting as a tool to accurately delineate putative ore deposits in covered terrain. The integration of microbial community information with soil chemistry and landscape development coupled with geology and geophysics appreciably improves the drill / no-drill decision process and has proven to be far more accurate than traditional surficial exploration methods, alone. There is high potential for application as a field-based technique as microbial databases for kimberlites and porphyry deposits are refined, and as sequencing technology is progressively developed into portable platforms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".