Misinterpretation of Drinking Water Quality Monitoring Data with Implications for Risk Management
Bibliographic record
Abstract
A survey of two groups of environmental professionals was conducted to explore the degree of understanding in the interpretation of monitoring results for informing decision-making and responding to data appropriately to manage environmental health risks. Specifically, the understanding of the predictive value of a monitoring result and the appreciation that false positive results will inevitably predominate when monitoring for rare or infrequent hazards was explored. Results indicate evidence of misinterpretation and overconfidence in the meaning of monitoring results, and the ability of laboratory methods to detect reliably an infrequent hazard in environmental samples. A hypothetical monitoring scenario was presented with characteristics sufficient to estimate what level of confidence was warranted in a positive result. The majority of respondents in both groups (most of whom had more than 10 years experience in their field) reported between very likely to almost certain confidence (80-100% likelihood) in a hypothetical monitoring result which was, in fact, less than 5% likely to be correct. Additionally, there was little influence of the beliefs expressed about the validity of the monitoring result on the actions proposed to be taken in response to finding that monitoring result. The independence of respondents' follow-up action to what they believe of a monitoring result implies a level of detachment between the understanding of the monitoring data and the resulting risk management response.
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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 | 0.033 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".