Bibliographic record
Abstract
Having recently published an article in AWWA Water Science (AWS), Leili Abkar answered questions from AWS editor-in-chief Kenneth L. Mercer about the research. Sedimentation: Hydraulic Improvement of Drinking Water Biofiltration Leili Abkar, Amina K. Stoddart, and Graham A. Gagnon I believe that accessible, safe, and affordable drinking water is a human right. My home country, Iran, is increasingly suffering from water shortages and I wanted to be part of the solution. I originally started with desalination research and now crossed over to drinking water biofiltration. I find the addition of microbiology to a process engineering perspective in drinking water to be fascinating. I think this field is rapidly evolving and has a major impact on every person's day-to-day life. I am part of a large and multidisciplinary team at the Center for Water Resources Studies , Dalhousie University. My research aims to understand biofiltration from macro to micro levels. This includes dealing with engineering issues, such as implementing a clarification process, and with biological aspects, such as studying the role of the microbial community in a drinking water biofilter. The biological aspects also include doing microbial community analyses, investigating the microscopic biofilm structures, and relating bacteria responses to different environmental conditions that can be used in drinking water plants. I believe my curiosity, resiliency, and self-reflectiveness, such as being open to feedback, will help me advance in my research. Also, asking the right questions has helped to facilitate and direct me to the right solutions. I have been blessed with many mentors whose diverse advice and guidance contributed to my development. The advice that stands out most to me was from two of my mentors, Dr. Ghassemi and Dr. Gagnon, who said that success will follow when you follow this advice: “When life gives you lemons, make lemonade.” This means that regardless of what happens, your attitude, actions, and perseverance make your life a beautiful story. Many water utilities are struggling to extract maximum benefits from biofiltration. I was working at the JD Kline Water Treatment Plant (as part of the Water Research Foundation's #4555 project), which uses a direct filtration system. From my experience, a simple yet impactful question arises: how could a clarification step in biofiltration affect performance? We wanted to see if it could improve the hydraulic performances, which would lead us to longer filter runtime and reduced costs from decreased shutdown time and backwashing. We investigated the effects of sedimentation as a clarification process on a pilot scale. This recent paper is the result of the investigation in a pilot-scale plant, which can be translated to full-scale biofilters. The next step is a cost analysis that weighs a capital cost of installing the sedimentation tank versus operational cost savings. I enjoy hiking in nature. Nature is a source of inspiration for me and it helps me to ground myself. I look at hiking as more than just an exercise but a sort of meditation method. Also, I attend choregraphed dance classes. Dance for me is a way that I can express myself and makes me feel free and vibrant. To learn more about this research, see the abstract on page 19 and visit the article, available online at https://doi.org/10.1002/aws2.1160.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.168 | 0.123 |
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".