Recommended best practices for plastic and litter ingestion studies in marine birds: Collection, processing, and reporting
Bibliographic record
Abstract
Marine plastic pollution is an environmental contaminant of significant concern. There is a lack of consistency in sample collection and processing that continues to impede meta-analyses and large-scale comparisons across time and space. This is true for most taxa, including seabirds, which are the most studied megafauna group with regards to plastic ingestion research. Consequently, it is difficult to evaluate the impacts and extent of plastic contamination in seabirds fully and accurately, and to make inferences about species for which we have little or no data. We provide a synthesized set of recommendations specific for seabirds and plastic ingestion studies that include best practices in relation to sample collection, processing, and reporting, as well as highlighting some “cross-cutting” methods. We include guidance for how carcasses, regurgitations, and pellets should be handled and treated to prevent cross-contamination, and a discussion of what size class of microplastics can be assessed in each sample type. Although we focus on marine bird samples, we also include standardized techniques to remove sediment and biological material that are generalizable to other taxa. Lastly, metrics and data presentation of ingested plastics are briefly reviewed in the context of seabird studies.
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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 | 0.276 | 0.487 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.017 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".