Tracing Molybdenum Attenuation in Mining Environments Using Molybdenum Stable Isotopes
Bibliographic record
Abstract
Molybdenum contamination is a concern in mining regions worldwide. Better understanding of processes controlling Mo mobility in mine wastes is critical for assessing potential impacts and developing water-quality management strategies associated with this element. Here, we used Mo stable isotope (δ 98/95 Mo) analyses to investigate geochemical controls on Mo mobility within a tailings management facility (TMF) featuring oxic and anoxic environments. These isotopic analyses were integrated with X-ray absorption spectroscopy, X-ray diffraction, Raman spectroscopy, transmission electron microscopy, and aqueous chemical data. Dissolved Mo concentrations were inversely correlated with δ 98/95 Mo values such that enrichment of heavy Mo isotopes in solution reflected attenuation processes. Inner-sphere complexation of Mo(VI) with ferrihydrite was the primary driver of Mo removal and was accompanied by a ca. 1‰ isotope fractionation. Limited Mo attenuation and isotope fractionation were observed in Fe(II)- and Mo-rich anoxic TMF seepage, while attenuation and isotope fractionation were greatest during discharge and oxidation of this seepage after discharge into a pond where Fe-(oxyhydr)oxide precipitation promoted Mo sorption. Overall, this study highlights the role of sorption onto Fe-(oxyhydr)oxides in attenuating Mo in oxic environments, a process which can be traced by Mo isotope analyses.
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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 | 0.000 | 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 | 0.000 |
| 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 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".