Aquatic micro‐ and nano‐plastics in life cycle assessment: Development of an effect factor for the quantification of their physical impact on biota
Bibliographic record
Abstract
Abstract Plastic litter of all sizes has been acknowledged as a serious threat to biodiversity, especially in the marine environment. The fact that life cycle assessment (LCA) does not properly consider these issues is a serious problem for the aspirations of LCA in the public sphere. This paper focuses on micro‐ and nano‐sized plastics (MNPs), which have the potential to cause a substantial impact on ecosystem quality because of their increased presence in the marine compartment and capacity to affect a greater range of species. The data regarding MNPs’ effect on different aquatic species were extracted from the academic literature. These data were then explored and analyzed to bring to light the possibilities in terms of effect factor (EF) developments and the existing relations between effect on aquatic ecosystems and different parameters such as particle size, polymer type, and shape. No significant difference could be observed between the effect of the different subgroups of MNPs tested when considering a single species. However, when including many species in the analysis, differences could be noted between polystyrene (PS) and other polymer types. The high uncertainty on the developed EFs combined with this lack of statistical difference among subgroups at the single species level suggest that the use of a single generic EF could be appropriate for now. This EF is provided along with Species Sensitivity Distributions developed to allow for a quick visualization of the gathered data used to generate the EFs. This EF can now be used to quantify the physical impact of all MNPs in life cycle impact assessment.
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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 | 0.001 | 0.001 |
| 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".