Experimental Investigation of Colloid-Facilitated Metal Transport in Mine-Impacted Wetland Sediment
Bibliographic record
Abstract
Metal mining operations can release toxic metals to surrounding environments, often necessitating remediation.Contaminant transport can increase the area impacted, but site-specific conditions control the movement of contaminants.Colloid-facilitated transport, the transport of contaminants with small, mobile particles, has been recognized as a potential contaminant transport vector in groundwater, but it remains unclear whether it is important in all situations.This work presents two laboratory experiments that study the effect of colloids on metal mobility in saturated, wetland sediment using mixed and single metal solutions and neutral to acidic solution pHs.Results indicate that colloid-facilitated transport is only important when small, humic acid colloids are present and at pH ≥4.Larger particles were found to be largely immobile, so could aid in the immobilization of metal contaminants.These findings imply that colloid-facilitated transport is important in wetland sediment and should be considered when remediating mine sites.Devon Geological Services for approving access to the Ore Chimney property for collection of sediment samples, and thanks go to fellow graduate student Mitchell Richardson for his friendship throughout my degree and for his help with getting all the columns tightened.Enormous thanks go to my parents Heather and Darren Harper, and my sister Meagan Harper for their support and help throughout this project.They made a fantastic field team and their willingness to wade through a wetland and carry large buckets of sediment samples out from the field made field-work safe and possible during the early parts of the COVID-19 pandemic.I also wish to thank them for rearranging our house to give me a fantastic workspace so I could begin my research at home.
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 | 0.000 | 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".