Plastic Pirates Nova Scotia 2024 dataset - citizen science investigation of anthropogenic litter pollution of aquatic environments
Bibliographic record
Abstract
This dataset describes anthropogenic litter pollution, including plastic pollution, at nine sampling sites at lakes and the ocean in Nova Scotia in the fall of 2024. It comprises data from two different citizen science protocols. The first protocol ("Group A") assessed litter density per m² and litter type (paper, cigarettes, plastic, metal, glass, food leftover, other). A total of 675 m² were investigated using this protocol, and a total of 495 litter items classified. The second protocol ("Group B") assessed the litter item composition of individual items, based on 24 litter categories, including nine single-use plastics categories. In total 3032 litter items were collected and sorted into these categories, representing 114 kg of litter. In total, 20 sampling activities (Group A and B combined) were conducted at nine sampling sites. Potential sources of litter items were also evaluated, and each sampling site was described in detail, including coordinates, shape, slope and orientation of the shoreline and accessibility. The data were collected by 275 people from nine organizations, most of them citizen scientists. Collectively, they contributed 635 h of data collection effort, including litter collection, litter sorting and data annotation. Most of the citizen scientists were schoolchildren, aided in their research by their teachers and the coordinators of this project. The implementation of the project in Nova Scotia was part of the Plastic Pirates program (https://www.plastic-pirates.eu/), investigating litter pollution in different countries in cooperation with schoolchildren and teachers. This open access dataset, available on Zenodo (https://doi.org/10.5281/zenodo.16949607) [1] under Creative Commons license CC BY 4.0, can be used to compare litter densities and litter composition across regions in Canada or worldwide, and is of value as a reference data point in time to assess litter pollution in temporal litter studies.
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 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".