How much should we care about insect–plastic interactions?
Bibliographic record
Abstract
The world relies heavily on plastic use in daily life, leading to increased global concern over mismanagement of plastic waste, its entry into natural environments, and impacts on living organisms. Over time, plastic in the environment will break down into microplastics (5 mm-1 µm) and eventually into nanoplastics (<1 µm), which are found in many living organisms, including insects. Insects are also of particular interest in plastic waste management because of their potential role in degrading plastic waste. However, these applications have not yet been scalable, and the ubiquity and consequences of plastic ingestion are unclear. Further, insect-plastic interactions are complicated by the seemingly endless combinations of shapes, types, sizes, and concentrations of plastics. As a result, we have a fragmented body of literature and unclear patterns that raise questions about whether resources put toward studying insect-plastic interactions should be placed elsewhere and why, or how much, we should care. Nevertheless, insects are vital members of almost all ecosystems, and their populations are already threatened by numerous stressors; thus, ignoring another potential threat would be unwise. To reveal clear patterns that can shape how we invest in mitigating and harnessing insect-plastic interactions, we pose six major questions. We also present a matrix of 'care' that combines the likelihood of exposure with the strength of the outcome of the interaction. We aim for these questions and matrix to serve as tools to guide broader participation, research priorities, and allocation of resources, to tackle what is currently a prodigious, but worthy, pursuit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".