Bibliographic record
Abstract
Research Article| November 01 2005 Monitoring aluminium after filtration Denis Bérubé; Denis Bérubé 1Environmental Health Centre, Health Canada, Tunney's Pasture, 0800B3, Ottawa, Ontario Canada, K1A 0L2 E-mail: denis_berube@hc-sc.gc.ca Search for other works by this author on: This Site PubMed Google Scholar Mark Soucy Mark Soucy 1Environmental Health Centre, Health Canada, Tunney's Pasture, 0800B3, Ottawa, Ontario Canada, K1A 0L2 Search for other works by this author on: This Site PubMed Google Scholar Journal of Water Supply: Research and Technology-Aqua (2005) 54 (7): 463–473. https://doi.org/10.2166/aqua.2005.0043 Article history Received: May 03 2005 Accepted: August 01 2005 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Twitter LinkedIn Tools Icon Tools Cite Icon Cite Permissions Search Site Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll JournalsThis Journal Search Advanced Search Citation Denis Bérubé, Mark Soucy; Monitoring aluminium after filtration. Journal of Water Supply: Research and Technology-Aqua 1 November 2005; 54 (7): 463–473. doi: https://doi.org/10.2166/aqua.2005.0043 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Aluminium speciation was characterized to examine the changes after filtration, in drinking water systems with low (≤56 μg l−1) and high (>100 μg l−1) aluminium concentrations at plant outlet. The treatment chemicals were also examined, and only lime showed significant aluminium contamination. This contamination varied from 0.4 to 2.5 μg mg−1 and was largely water soluble (mean: 85%). The amounts of aluminium in lime added after filtration were equivalent to water concentrations ranging from 8.6 to 34 μg l−1 (mean: 17.5 μg l−1). By using the less contaminated lime supply, the aluminium concentration contributions could have been four times lower (mean: 4.1 μg l−1). When determining the concentration differences between the effluents of one filtering unit and plant outlet, the aluminium changes at the nine low-aluminium plants were associated with lime addition, and were mostly detected in soluble form. The changes at the eight high-aluminium plants paralleled problems occurring at previous stages and were mostly attributed to variations occurring at additional filtering units. These situations prevented detection of other changes (e.g. post-precipitation). The monitoring of leachable particulate aluminium and of various parameters (pH, turbidity, Al–turbidity relationship) was also useful in differentiating particulate matter and demonstrating the effects of undesirable lime properties (dissolution delays, particulate residues). additive contamination, drinking water treatment, lime, particulate aluminium, soluble aluminium, turbidity This content is only available as a PDF. © Health Canada 2005 You do not currently have access to this content.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".