When Microplastic Is Not Plastic: The Ingestion of Artificial Cellulose Fibers by Macrofauna Living in Seagrass Macrophytodetritus
Bibliographic record
Abstract
Dead leaves of the Neptune grass, Posidonia oceanica (L.) Delile, in the Mediterranean coastal zone, are colonized by an abundant "detritivorous" invertebrate community that is heavily predated by fishes. This community was sampled in August 2011, November 2011, and March 2012 at two different sites in the Calvi Bay (Corsica). Ingested artificial fibers (AFs) of various sizes and colors were found in 27.6% of the digestive tracts of the nine dominant species regardless of their trophic level or taxon. No seasonal, spatial, size, or species-specific significant differences were revealed; suggesting that invertebrates ingest AFs at constant rates. Results showed that, in the gut contents of invertebrates, varying by trophic level, and across trophic levels, the overall ingestion of AFs was low (approximately 1 fiber per organism). Raman spectroscopy revealed that the ingested AFs were composed of viscose, an artificial, cellulose-based polymer. Most of these AFs also appeared to have been colored by industrial dyes. Two dyes were identified: Direct Blue 22 and Direct Red 28. The latter is known for being carcinogenic for vertebrates, potentially causing environmental problems for the P. oceanica litter community. Techniques such as Raman spectroscopy are necessary to investigate the particles composition, instead of relying on fragment size or color to identify the particles ingested by animals.
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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) | 0.001 | 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".