American Chemical Society AMA: I am Susan D. Richardson, Ph.D., a Professor of Chemistry at the University of South Carolina and expert on water treatment chemistry. Ask me anything about the chemistry of swimming pool disinfection!
Bibliographic record
Abstract
Hi Reddit! Ask me anything about the chemistry of disinfecting water for swimming pools or other treatment needs!* I’m Susan D. Richardson, Ph.D., the Arthur Sease Williams Professor of Chemistry in the Department of Chemistry and Biochemistry at the University of South Carolina in Columbia, SC. Prior to coming to USC in January 2014, I was a research chemist for several years at the U.S. Environmental Protection Agency’s National Exposure Research Laboratory in Athens, GA. For the last several years, I’ve been conducting research in drinking water and in swimming pool water—specifically in the study of toxicologically important disinfection by-products (DBPs). These are the unintended consequence of trying to kill harmful microorganisms in drinking water and in pools. The disinfectants kill bacteria and contaminants that can cause deadly illnesses such as cholera, but they can interact with natural organic matter formed from decaying leaves and plants in rivers. Disinfectants like chlorine will react with that natural organic matter to form DBPs, that can in turn cause detrimental human health effects in drinking water, including bladder cancer, miscarriages, and birth defects. Pool DBPs have also been implicated in cases of asthma and bladder cancer. I work on identifying new DBPs or other unknown chemicals in the environment, drinking water, and pools using mass spectrometry. I also study wastewater treatment plants and the effects of disinfectants on river water. Most recently, I’ve started to investigate the impact of hydrofracking on DBPs in water. You can read about some of my newest research on DBPs in swimming pool and spa water in this Editor’s Choice open access article in Environmental Science & Technology and my work is also covered in a recent article from Chemical & Engineering News. My B.S. in chemistry and mathematics is from Georgia College & State University and my Ph.D. in chemistry is from Emory University. I also have an honorary doctorate from Cape Breton University in Canada and was recently named an ACS Fellow. I serve as an Associate Editor for Water Research, on the Editorial Advisory Board for Environmental Science & Technology, and write on emerging contaminants in water/environmental analysis for Analytical Chemistry. I’ll be on at 1pm EDT. I’m live now! I look forward to answering your questions! -sr
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.540 | 0.457 |
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".