Refining the use of stream biofilms as a pesticide monitoring tool: impact of sample processing and periphytometer replication on detection accuracy
Bibliographic record
Abstract
Accurate characterization of pesticide exposure is critical for assessing risks to aquatic ecosystems. However, pesticide concentrations in surface waters are highly variable and often missed by traditional grab sampling methods, which typically fail to capture transient peaks and hydrophobic compounds that partition into non-water matrices. Time-integrated monitoring approaches, such as biofilm sampling using periphytometers, offer a promising alternative. Despite this, uncertainties remain about how biofilm processing methods affect pesticide quantification, and whether replicate periphytometers are necessary to improve monitoring accuracy. To address these questions, water and biofilm samples were collected from six stream sites in southern Ontario. Biofilm samples were split and processed using either freeze-drying or centrifugation to evaluate the effect of sample preparation on pesticide detection. All samples were analyzed for approximately 500 pesticides. Freeze-dried biofilm consistently yielded higher pesticide detection frequencies and concentrations compared to centrifuged samples, indicating that freeze-drying is a more effective processing method for capturing pesticide residues in biofilm. These findings highlight the importance of standardizing biofilm processing methods to improve monitoring reliability. To evaluate the need for replication, six periphytometers were deployed in parallel at a single stream site. Biofilm samples from each replicate were analyzed for pesticide presence. Pesticide detection profiles were highly consistent across replicates (p > 0.05) in two separate sampling events, suggesting that a single periphytometer is sufficient to characterize site-level pesticide exposure at our stream site. Together, these results provide guidance for improving pesticide monitoring programs by recommending freeze-drying for biofilm processing and supporting the continued use of single periphytometer deployments, which are more feasible for routine monitoring without compromising data quality.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".