Evaluating mineralogical, geochemical and microbial relationships within sulfur-bearing mine wastes; a multianalytical and multivariate statistical approach
Bibliographic record
Abstract
Mine tailings harbour a dense population of extremophiles that play key roles in metal and mineral \ntransformations. While microbially mediated mineral oxidation and reduction within sulfurbearing \nmine waste has been widely reported, linkages and statistical significance between \nenvironmental parameters and geochemical and mineralogical compositions with microbial \ncommunity diversity has not yet been documented. A combined geochemistry, mineralogy, and \ngenomics approach has been used in this study to better understand the biogeochemical processes \noccurring within a gradient of tailings dam materials both at surface and down a depth profile. \nComplex microbial diversity patterns across sample types, environmental conditions, spatial \nlocations, and geochemical and mineralogical compositions, are addressed using multivariate \nstatistical analysis. Additionally, scanning electron microscopy (SEM) and transmission electron \nmicrscopy (TEM) are used to analyze the compositional and morphological features of microbemineral \nassemblages. A total of 40 sulfur-bearing tailings samples have been collected aseptically \nfrom five constructed tailing dam structures, in Sudbury, Ontario. Samples have been grouped into \nthree zones including oxidized, transition, and unoxidized, which are distinguished by pH, munsell \ncolour and mineralogical composition. Oxidized material is composed of silicates and ironhydroxides \n(goethite) and iron-hydroxy sulfate minerals (jarosite, schwertmannite), with contact \npH ranging from 2.6 - 4.5. Material from the unoxidized zone consists mainly of silicates and \nsulfides (pyrite, pyrrhotite, chalcopyrite and greigite), with a higher contact pH ranging from 4.14 \n- 5.71. Transitional material is dominantly composed of silicates, but contains both sulfides and \nsecondary iron and sulfate minerals, with pH ranging from 3.6 - 5.31. Microbial community \ncomposition and structures are often attributed to their surrounding geochemical environments. \nHowever, no significant correlations are observed between total metal content and microbial \ncommunity compositions across the analyzed samples. However, community compositions and \nstructures exhibit significant changes based on the contact pH ranges and mineralogical \niv \ncompositions of the analyzed tailings material. Oxidized, transition, and unoxidized material \nexhibit similar taxa, however, relative abundance of taxa exhibits extensive variability across the \nidentified zones. Key indicator species are statistically tested across the three alteration zones, with \nmicroorganisms whose metabolic functions underpin iron and/or S oxidation and/or reduction \nidentified as microorganisms characterizing alteration zones. Microbial-mineral assemblages \nanalyzed at the nano-scale exhibit minerals that were not identified by routine whole sample \nanalysis (XRD), thereby indicating the importance of performing both SEM and TEM to fully \nunderstand microbial-mineral interactions. The microbial-mineral relationships observed in this \nstudy both at the micro- and nano-meter scale have resulted in furthering understanding of \nbiogeochemical processes occurring within the analyzed sulfur-bearing tailings material.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".