Assessing size-based exposure to microplastic particles and ingestion pathways in zooplankton and herring in a coastal pelagic ecosystem of British Columbia, Canada
Bibliographic record
Abstract
Microplastic particles (hereafter 'microplastics') are a widespread class of pollutants in marine environments that can become embedded in food webs. Due to their diverse composition and size, microplastics can enter food webs both directly through consumption and indirectly via trophic transfer. In this study, we investigated potential ingestion pathways of microplastics in an important pelagic food web in coastal British Columbia, Canada. Between March and September 2019, we completed repeat surveys of water, zooplankton, and larval Pacific herring Clupea pallasii at 11 locations in Baynes Sound, Strait of Georgia. Five zooplankton taxa were isolated from each zooplankton sample for specific analysis. Juvenile herring were sampled once in September. Samples were cold-digested with KOH or H 2 O 2 and suspected microplastics isolated. Suspected microplastics were confirmed using μ-Raman spectroscopy and were subsequently identified from the collected samples. The average microplastic concentration in surface waters was 0.59 microplastic particles l -1 , and no clear spatial pattern was evident. Average microplastic particle loads were 0.0007 ind. -1 in zooplankton, 0.0017 ind. -1 in larval herring, and 0.089 ind. -1 in juvenile herring. There was a clear difference in the biological:microplastic particle ratio across size fractions (125-250, 250-500, 500-1000, 1000-2000, 2000-4000 µm) in the water column. In size classes <1000 µm, biological particles outnumbered microplastic particles by up to 4 orders of magnitude, whereas for size classes >1000 µm, the ratio decreased to nearly 1. Zooplanktivorous consumers like juvenile herring are more likely to consume microplastics than prey since the ratio of microplastic particles >1000 µm to potential food, and therefore encounter rate, is higher.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".