Tracking radiolabelled polystyrene microplastics in young Atlantic scallop (Placopecten magellanicus): bioaccumulation, depuration and bioenergetic impacts assessment
Bibliographic record
Abstract
The massive production and use of plastics have resulted in their release, under various forms, in marine ecosystems, now a major environmental pollution concern. Degradation and fragmentation processes break down plastics into fine particles, called microplastics (MPs) and nanoplastics (NPs), whose bioaccumulation and effects on marine organisms remain largely unknown. This study investigates the long-term bioaccumulation and depuration kinetics as well as the biodistribution of 1.5 ± 0.3 μm polystyrene microparticles (PS-MPs) and assesses their impact on the energy reserves of young non-reproductive Atlantic scallops ( Placopecten magellanicus ). Three treatments were used during a three-month exposure period; organisms fed with a mixture of microalgae and 14 C-labelled PS ( 14 C-PS-MPs) at concentrations close to those expected in the marine environment; organisms on the same treatment but with non-radiolabelled PS-MPs, and control organisms fed only a mixture of microalgae. Thereafter, depuration was monitored over 3 months, scallops receiving only microalgae. Autoradiographic data showed that 14 C-PS-MPs ingested by scallops were concentrated in the digestive tract. 14 C-PS-MPs levels measured in samples of hepatopancreas (6300 ± 4900 Bq•g −1 ww ) and muscle (85 ± 81 Bq•g −1 ww ) exhibited large interindividual variability and no temporal trend during exposure. Upon depuration, 91 % of 14 C-PS-MPs in scallops was eliminated in less than 0.65 d and little radioactivity remained in hepatopancreas and muscle after 7 d (<200 and < 20 Bq•g −1 ww , respectively). Finally, the consumption of non-labelled PS-MPs had no detectable impact on the energy reserves of scallops such as lipid and glycogen concentrations.
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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".