Impacts of Sand and Dust Storms on Oceans
Bibliographic record
Abstract
New Scientific Report – Impact of Sand and Dust Storms on Oceans. Each year, an estimated two billion tonnes of sand and dust is raised into the atmosphere; and one-quarter of this reaches the oceans. With the aim to help policy-makers in their efforts to tackle this widespread challenge, UNEP’s Science Division assessed the latest scientific knowledge on the impact of these large dust clouds on the oceans. A problem that affects the health of marine systems around the world and the well-being of the people that depend on them for their survival. Hence, on the 6th of November 2020, UNEP launched its report, titled Impacts of Sand and Dust Storms on Oceans: A Scientific Environmental Assessment for Policy Makers. It was authored by University of Oxford scientist Nick Middleton and reviewed by dozens of experts from around the globe. During the development of the report, UNEP received technical support from the GESAMP, the GPA and the GPNM. It was published within the framework of the UN Decade of Ocean Science for Sustainable Development 2021-2030.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".