Microstructural control on the trace element distribution and Au concentration in pyrite nodules
Bibliographic record
Abstract
The crystal growth history of an Au-rich sedimentary pyrite nodule from the Timmins-Porcupine Au camp, Ontario, Canada, has been investigated using Electron Backscattered Diffraction and Laser Ablation Inductively Coupled Plasma Mass Spectrometry techniques to study the crystallographic processes controlling metal deportment in the pyrite structure. Results show four distinct growth stages characterized by different pyrite microstructures, crystal forms and trace element compositions. A direct link is observed between the growth of octahedral facets in pyrite and the development of primary (non-tectonic) subgrain boundaries. Furthermore, zones with a high abundance of subgrain boundaries have the highest Au, As, Ag and Cu (and other metals) contents – suggesting metal distribution is linked to the development of microstructures. Finer-grained aggregates are characterized by higher grain boundary density than in coarse areas, making higher trace element concentrations inversely proportional to grain size. Our results indicate that the high Au concentrations (~100 ppm) in pyrite represent a primary feature related to nodule growth, instead of secondary enrichment processes, and highlight the possibility that sediment-hosted pyrite nodules could represent a metal-rich geochemical reservoir for the formation of younger orogenic Au deposits. • Microstructures in diagenetic pyrite nodules are linked to metal incorporation. • Au, As, Ag, and Cu are concentrated in non-tectonic grain and subgrain boundaries. • Grain and subgrain boundaries are interpreted as primary growth structures. • The incorporation of Au, As, Ag, is a primary feature in diagenetic pyrite nodules. • High S content during diagenesis is related with higher Au incorporation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 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".