Biogeochemical Characterization of Metal Behavior from Novel Mussel Shell Bioreactor Sludge Residues
Bibliographic record
Abstract
Acid mine drainage (AMD) remediation commonly produces byproducts which must be stored or utilized to reduce the risk of further contamination. A mussel shell bioreactor has been implemented at a coal mine in New Zealand, which is an effective remediation option, although an accumulated sludge layer decreased efficiency which was then removed and requires storage. To understand associated risks related to storage or use of the AMD sludge material, a laboratory mesocosm study investigated the physio-chemical and biological influence in two conditions: anoxic storage (burial deep within a waste rock dump) or exposure to oxic environments (use of sludge on the surface of the mine). Solid phase characterization by Scanning Electron Microscopy (SEM) and selective extraction was completed to compare two environmental conditions (oxic and anoxic) under biologically active and abiotic systems (achieved by gamma irradiation). Changes in microbial community structure were monitored using 16s rDNA amplification and next-generation sequencing. The results indicate that microbes in an oxic environment increase the formation of oxyhydroxides and acidic conditions increase metal mobility. In an oxic and circumneutral environment, the AMD sludge may be repurposed to act as an oxygen barrier for mine tailings or soil amendment. Anoxic conditions would likely promote the biomineralization of sulfide minerals in the AMD sludge by sulfate reducing bacteria (SRB), which were abundant in the system. The anoxic conditions reduced the risk of trace metals (Zn) associated with oxides, but increased Fe associated with organic material. In summary, fewer risks are associated with anoxic burial but repurposing in an oxic condition may be appropriate under favorable conditions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".