<i>AWWA Water Science</i> Author Spotlight
Bibliographic record
Abstract
Having published an article in AWWA Water Science, Javier Locsin answered questions from the publication's editor-in-chief, Kenneth L. Mercer, about the research. Potential Regulatory Implications of Health Canada's New Lead Guideline Javier M. Locsin, Benjamin F. Trueman, Dallys Serracin-Pitti, Gillian M.L. Stanton, and Graham A. Gagnon Javier enjoys bingsu at a restaurant in Vancouver. I was raised with a strong work ethic, which has been monumentally important for maintaining my momentum in graduate school. Most importantly, my natural curiosity has led me to places and people that have positively affected my career. I was fortunate to have three mentors—Dr. Graham Gagnon, my PhD supervisor, taught me how to design and execute rigorous academic research and write up results as a story that can be both analytically critical and interesting to read. Dr. Benjamin Trueman taught me how to connect fundamental science with practical application. Dr. Evelyne Doré provided often humorous advice about how to survive academia but most importantly, to embrace the chaos in learning. Health Canada released an updated Lead in Drinking water guidance document in 2019. The main changes were the reduction of the maximum allowable lead concentration from 10 to 5 μg/L and the replacement of flushed sampling with either random daytime or 30-min stagnation sampling. The two new sampling protocols were expected to capture higher lead concentrations and increase the likelihood of a utility being out of compliance with regulations. To prepare for the changes and assess their effects on utilities in Nova Scotia, we partnered with Nova Scotia Environment, as well as large and small water utilities, to understand lead levels in drinking water. We compared old and new sampling protocols for measuring lead at the tap. First is the communication and implementation of water research. Advances in research may happen quickly, and we need to more effectively distribute this information. Second is identifying sustainable alternatives to current practices. Finally, educating and encouraging our communities to actively participate in the field of water resource management will be a challenge, whether it be through source water protection or monitoring, policy, or treatment practices. Javier, his parents, and sister have dinner during a visit to Prince Edward Island. I grew up in the Philippines but was fortunate to travel and be exposed to many different places and cultures. Each of these places experienced different water challenges and applied varied approaches in addressing them. I gained a unique perspective on managing issues. Bouldering or scuba diving can provide relief from my everyday analytical thinking and still stimulate my problem-solving skills. I enjoy listening to music while reading a good book and hanging out with my dog, Fisher. I am also a foodie; I believe that food is one of the best ways to experience new cultures. To learn more about Javier's research, visit the article, available online at https://doi.org/10.1002/aws2.1182.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.373 | 0.287 |
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".