Introducing 'DeNIS': a global database on anthropogenic marine Debris and Non-Indigenous Species
Bibliographic record
Abstract
Anthropogenic marine debris (AMD) poses a major threat to marine life, biodiversity, and ecosystems, which is particularly alarming due to its growing abundance, durability, and persistence in the marine environment. In addition to well-studied impacts on marine organisms' health and survival, recent research indicates an additional but less obvious impact: AMD facilitates long-distance and even transoceanic dispersal, acting increasingly as a vector for transport and introduction of non-indigenous species (NIS) globally. AMD may facilitate new introductions but also promote secondary spread of invasions, compounding even further its ecological impact in marine ecosystems. Around the world, opportunistic and targeted sampling has already provided extensive information on marine debris as a vector and the associated species. However, the information is mostly scattered and with no systematic organization or curation. In this context, we launched 'DeNIS': a global database on marine Debris and Non-Indigenous Species designed to compile crucial information on AMD and its epibionts. DeNIS was developed on an easy-to-use platform for data synthesis and functional visuals, integrating past and ongoing measures, and includes a back-office interface for data gathering, classification and rigorous analysis.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".