Deep-sea ecosystems of the North Atlantic Ocean: discovery, status, function and future challenges
Bibliographic record
Abstract
The North Atlantic is an ocean basin with a diversity of deep-sea ecosystems. Here we provide a summary of the topography and oceanography of the North Atlantic including the Gulf of Mexico and Caribbean Sea, provide a brief overview of the history of scientific research therein, and review the current status of knowledge of each of 18 pelagic and benthic deep-sea ecosystems, with a particular focus on knowledge gaps. We analyse biodiversity data records across the North Atlantic and highlight spatial data gaps that could provide important foci for future expeditions. We note particular data gaps in EEZs of nations within and bordering the Caribbean Sea. Our data provide a baseline against which progress can be tracked into the future. We review human impacts caused by fishing, shipping, mineral extraction, introduction of substances, and climate change, and provide an overview of international, regional and national measures to protect ecosystems. We recommend that scientific research in the deep sea should focus on increasing knowledge of the distribution and the connectivity of key species and habitats, and increasing our understanding of the processes leading to the delivery of ecosystem services. These three pillars - distribution, connectivity, ecosystem function - will provide the knowledge required to implement conservation and management measures to ensure that any deep-sea development in the future is sustainable. Infrastructure and capacity are unevenly distributed and implementation of strategies that will lead to more equitable deep-sea science is required to ensure that essential science can be delivered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".