Building evidence around ghost gear: Global trends and analysis for sustainable solutions at scale
Bibliographic record
Abstract
Abandoned, lost or discarded fishing gear (ALDFG) comprises a significant amount of global marine debris, with diverse impacts to marine environments, wildlife, and the fishing industry. Building evidence on ALDFG is critical to holistically understand the marine debris issue, and to inform the development of solutions that reduce amounts of ALDFG sources and recover existing gear. Substantial work has been and continues to be undertaken around the world to collect data on ALDFG, much of which remains unpublished. To provide a global picture of data on ALDFG, we organized a technical session that brought together seven ALDFG leaders to share their expertise in data collection, retrieval, and awareness-raising. This paper summarizes the technical session to highlight: 1) case studies that feature innovative approaches to ALDFG data collection and retrieval; 2) examples of opportunities to fill data gaps and improve our understanding of wildlife ingestion of and entanglement in ALDFG; and 3) awareness-raising through the development of a publicly accessible global ALDFG database.
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 | 0.082 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.034 | 0.047 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.029 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".