Pharmaceuticals and personal care products alter growth and function in lentic biofilms
Bibliographic record
Abstract
Environmental context Pharmaceuticals and personal care products are routinely found in waters discharged from treatment plants and in surrounding aquatic ecosystems. Despite the widespread occurrence of these biologically active agents, there is limited understanding of their potential effects on key ecosystem processes such as primary production, ecosystem respiration and algal growth. This paper examines the effects of five common pharmaceuticals on the rates of these fundamental processes. Abstract Pharmaceutical diffusing substrates were used to study in situ responses of aquatic biofilms in an urbanised lentic ecosystem to five pharmaceutical and personal care products (PPCPs; caffeine, cimetidine, ciprofloxacin, diphenhydramine and metformin). The pharmaceutical diffusing substrates consisted of porous biofilm substrates placed atop a mass of agar amended with 2.5 mM of the PPCP compound of interest. Over 21 days, biofilms growing on the substrata were exposed to slow diffusion of the PPCP through the agar and porous substrate. Algal biomass was suppressed by exposure to diphenhydramine (–81 %) and ciprofloxacin (–50 %). Gross primary production was completely suppressed by diphenhydramine exposure but stimulated by caffeine (+39 %) and cimetidine (+46 %). For heterotroph biofilms, community respiration was suppressed by exposure to diphenhydramine (–24 %). To characterise PPCP exposure, rates of diffusion from the pharmaceutical diffusing substrates were measured at 10, 20 and 30 °C. Diffusion was Fickian for all compounds and all temperatures. Diffusion coefficients, D, were in the range 1.5 × 10–10 to 1.1 × 10–9 m2 s–1. From diffusion data, average release rates over 21 days were typically 30–50 ng min–1 cm–2 at 20 °C. The results show that PPCPs can dramatically affect rates of key ecological processes, and the relationship between release rate and ambient concentration of PPCPs is discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".