Bibliographic record
Abstract
Arsenic exists naturally and it is the 20th most abundant element in the earth's crust. The high Arsenic concentration in some of Newfoundland water sources could be due to the oxidation of Arsenic bearing minerals by the heavy precipitations and rain fall all over the year. Arsenic is carcinogenic and the United States Environmental protection agency and the Canadian Environmental Quality Guidelines limit set is 10 μg/l of Arsenic in drinking water. The main objective of this research was to find a cheap, efficient, and locally available material in Newfoundland that could remove Arsenic from water. Peat is an abundant material in Newfoundland, however, functional groups, which are the main constituent of peat do not react with Arsenic species. To improve peat properties, Fe was used to impregnate peat, react with the functional groups, get oxidized, and the impregnated Fe hydroxide and oxyhydroxide could then adsorb Arsenic. To detect the properties of horticultural peat, characterization tests were carried out for the untreated and impregnated peat. In addition, batch and column studies were conducted to test the peat capacity for Arsenic adsorption before and after impregnation. It was found that peat impregnated with 0.54 M ferrous chloride (Fe 0.54-Peat) was the best adsorbent and it could reduce the Arsenic level from 60 μg/l to less than 10 μg/l. A total of 9 liters were treated with Fe 0.54-Peat in a continuous flow system to a level below this one. The pH increased with the duration of the experiments, as conditions gradually become more reducing and the pH was the main factor controlling the Arsenic adsorption between pH 3 to 5. The two suggested mechanisms responsible for adsorption are inner sphere complex and coadsorption or a mix between ion exchange and complexation.
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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".