Introducing Ocean Conservancy’s Wildlife Impact Calculator and Plastic-Wildlife Impact Database
Bibliographic record
Abstract
Cleaning up plastic pollution is one of three interventions scientists agree are necessary to address our global plastic pollution crisis. There is much work to be done to curb plastic production and improve waste management, but how can we effectively bolster public participation in cleanups to achieve this lofty goal? Powered by robust scientific models, Ocean Conservancy’s Wildlife Impact Calculator tool illuminates the real-life positive impacts of individual coastal cleanups on marine megafauna including sea turtles, seabirds and marine mammals. This tool makes powerful science on the mortality risks of macroplastic ingestion publicly accessible and understandable. The best part is that the positive ocean impacts of even small-scale, individual plastic cleanups can be calculated with the simple click of a button. Our team is also currently working to elucidate the mortality impacts of marine animal entanglement with plastic pollution, which will similarly be modeled and converted into a public-facing tool in the coming months.Ocean Conservancy’s Wildlife Impact Calculator quantifies and demonstrates the direct impact individuals can have on helping protect wildlife when partaking in plastic pollution cleanup efforts. We believe public engagement with our novel tool will both deepen personal connections to the issue, and ultimately unleash broader engagement in the fight to reduce ocean plastic pollution. Additionally, with nearly 10,000 entries, our publicly-available Plastic-Wildlife Impact Database contains all the research on macroplastic ingestion that informed this model. Together, our new resources can be used conservationists and decision makers alike to evaluate the benefits of different interventions to prevent or remove plastic pollution and help inform management and reduction policies.
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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.023 |
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".