Monitoring litter and microplastics in Arctic mammals and birds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Plastic pollution has been reported to affect Arctic mammals and birds. There are strengths and limitations to monitoring litter and microplastics using Arctic mammals and birds. One strength is the direct use of these data to understand the potential impacts on Arctic biodiversity as well as effects on human health, if selected species are consumed. Monitoring programs must be practically designed with all purposes in mind, and a spectrum of approaches and species will be required. Spatial and temporal trends of plastic pollution can be built on the information obtained from studies on northern fulmars ( Fulmarus glacialis (Linnaeus, 1761)), a species that is an environmental indicator. To increase our understanding of the potential implications for human health, the species and locations chosen for monitoring should be selected based on the priorities of local communities. Monitoring programs under development should examine species for population level impacts in Arctic mammals and birds. Mammals and birds can be useful in source and surveillance monitoring via locally designed monitoring programs. We recommend future programs consider a range of monitoring objectives with mammals and birds as part of the suite of tools for monitoring litter and microplastics, plastic chemical additives, and effects, and for understanding sources.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it