Geobiological Cycling of Gold: From Fundamental Process Understanding to Exploration Solutions
Bibliographic record
Abstract
Microbial communities mediating gold cycling occur on gold grains from (sub)-tropical, (semi)-arid, temperate and subarctic environments. The majority of identified species comprising these biofilms are β-Proteobacteria. Some bacteria, e.g., Cupriavidus metallidurans, Delftia acidovorans and Salmonella typhimurium, have developed biochemical responses to deal with highly toxic gold complexes. These include gold specific sensing and efflux, co-utilization of resistance mechanisms for other metals, and excretion of gold-complex-reducing siderophores that ultimately catalyze the biomineralization of nano-particulate, spheroidal and/or bacteriomorphic gold. In turn, the toxicity of gold complexes fosters the development of specialized biofilms on gold grains, and hence the cycling of gold in surface environments. This was not reported on isoferroplatinum grains under most near-surface environments, due to the lower toxicity of mobile platinum complexes. The discovery of gold-specific microbial responses can now drive the development of geobiological exploration tools, e.g., gold bioindicators and biosensors. Bioindicators employ genetic markers from soils and groundwaters to provide information about gold mineralization processes, while biosensors will allow in-field analyses of gold concentrations in complex sampling media.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".