Monitoring litter on Arctic and subarctic shorelines: current status and next steps for monitoring programs
Bibliographic record
Abstract
Plastic pollution is ubiquitous, and the Arctic is no exception. One important step to understand the extent of the problem, and to monitor its impact is to have repeatable, comparable, and relevant measures across time and space that allow for the detection of marine litter trends. Arctic shorelines are a critical part of monitoring efforts. Pan-Arctic monitoring of litter on shorelines is also an essential component to examine global trends. Based on previous work examining litter in some regions of the Arctic, we suggest steps towards more harmonized protocols that include community-based monitoring, crowdsourced science programs, and science team-based surveys that are specific for the Arctic. Specifically, we recommend that shoreline survey sites for long-term monitoring be established where possible and be at least 50 m and surveys carried out at regular intervals of at least twice a year by any type of research team. Criteria for the selection of sites should be grounded in Indigenous and other local community and regional priorities, and should result in representation of both remote shorelines impacted by distant-source marine litter and shorelines impacted by more local sources. Results of any Arctic shoreline litter surveys should be made regularly available either through publications which include data sets, and/or accessible databases to promote regional comparisons and trend analysis across the pan-Arctic.
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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 | 0.000 |
| 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 | 0.000 |
| 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".