Use of a fluorescent molecular rotor probe for nanoplastics assessment in epiphytic biofilms growing on submerged vegetation of Lake Saint Pierre (St. Lawrence River)
Bibliographic record
Abstract
Studies on plastic pollution conducted in freshwaters mainly focused on monitoring plastic debris in the water column or in the sediments. Few studies have investigated the occurrence of plastic debris in benthic biofilms (periphyton). Yet, algal biofilms may potentially act as a sink for plastic debris, trapping it within their mucilaginous or filamentous matrix. Biofilms may also represent a source of plastic debris by sloughing when they become senescent. In addition, plastic debris accumulated within biofilms may enter the food web via primary consumers. Considering these observations, this study aims to quantify nanoplastics (NPs) accumulated in biofilms growing on aquatic vegetation from Lake Saint Pierre (LSP), a fluvial lake of the St. Lawrence River, and its archipelago. Biofilms were removed from submerged plants and the presence of NPs was assessed by spectrophotometry using the fluorescent molecular rotor probe 9(dicyanovinyl)-julolidine (DCVJ). The results of this study confirm the biofilms’ ability to act as a sink for NPs. Despite the fact that the determination of the absolute nanoparticle number and size distribution remains a challenge, we estimated a median concentration of 1.05 × 109 NP/mg of biofilm dry weight (DW) when using 100 nm polystyrene beads for calibration. Concentrations were significantly different between water masses, with higher concentrations in samples collected in the two lateral water masses compared to the central water mass. Our study provides, for the first time, a quantitative assessment of NPs from epiphytic biofilms in a large river under the influence of anthropogenic sources.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".