ATLAS: a trans-Atlantic assessment and deep-water ecosystem-based spatial management plan for Europe
Bibliographic record
Abstract
The ATLAS EU Horizon 2020 project, running from 2016 to 2020, has created a dynamic new partnership to assess the Atlantic's deep-sea ecosystems and Marine Genetic Resources and create the integrated and adaptive planning products needed for sustainable Blue Growth. ATLAS is gathering diverse new information on sensitive Atlantic ecosystems (including Vulnerable Marine Ecosystems (VMEs) and Ecologically or Biologically Sensitive Areas (EBSAs)) to produce a step-change in our understanding of their connectivity, functioning and responses to future changes in human use and ocean climate. Fully integrated spatial planning products built on basin and regional scales are needed to allow stakeholders to explore, and respond to, alternate scenarios of ocean dynamics and cross-sectoral Blue Growth. The EU FP7 MESMA (Monitoring and Evaluation of Spatially Managed Areas) framework is being applied in case studies spanning the deep waters of the EU, US, Canada and ABNJs (NAFO and NEAFC regulatory areas) against the backdrop of potential climate change, to develop marine spatial planning scenarios to support basin scale ecosystem based management. Initial plans for each case study are presented.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".