Bibliographic record
Abstract
Acidification of water bodies due to mining related activities is considered to be one of the greatest pollution challenges faced by the mining industry. While traditional monitoring methods have primarily addressed potential acidity stemming from the oxidation of mineral associated sulfides, they often overlook various aqueous sulfur compounds such as thiosulfate and polythionates which are known to occur in mine impacted wastewater (MIW) as by-products of sulfide mineral processing which may also generate acidity. Mines are hampered by the limitations of the currently available, inefficient, expensive and time-consuming analytical methods to monitor these reactive sulfur compounds that can pose acidity, toxicity and contamination risks to receiving environments if not mitigated by treatment technologies prior to discharge. This thesis presents results of a developed, expeditious, inexpensive and near real time method to quantify the total concentration of reactive sulfur species (Sreactive; all sulfur atoms of oxidation state less than 6+ (i.e., SO42-, Whaley Martin et al. 2020)) for aqueous samples. The results of this thesis provide a promising method for rapid determination of [Sreactive], compatible with field and laboratory based analytical methods, by difference between the initial SO42- concentration in a sample, prior to oxidation, and the final SO42- concentration taken after exposure to a strong oxidizing agent, which converts all Sreactive in a sample to SO42-. Results determined the method to be quantitative within a pH range of 5 to 9, using 200 µL 10% - 15% NaClO (Sigma Aldrich) as the oxidant to oxidize all Sreactive species present in a 10.0 mL sample to SO42- within a time period of 5 minutes.The final method developed uses a portable HACH DR2800 spectrophotometer and 10-15% NaClO (Sigma Aldrich) as the oxidant with a ratio of 200 µL oxidant per 10.0 mL of sample volume and a 5 minute reaction time. The method was determined to be quantifiable in a pH range of 5 to 9 for samples with SO42- concentrations ranging from 7 mg/L to 70 mg/L. Initial tests of the developed method on field samples yielded promising results, with spike recoveries generally within +/-15% of expected values in reference (unaffected) water, receving water, tailings reservoir water, pilot reclamation pit lake water, treatment pond water and mill effluent. As the HACH DR2800 has not been widely validated for use with MIW, results obtained by HACH DR2800 were compared to IC determined values throughout all stages of research to ensure accuracy and were generally found be in good agreement with the exception of treatment pond water, although spike recoveries were still within +/-15% of expected values. Additionally, the method successfully detected naturally occurring baseline Sreactive in all field samples analyzed, including the unaffected natural waterbody samples, indicating potential for broader utility of this method for determination of Sreactive in a variety of contexts.
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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) | 0.001 | 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".