Editorial: Seafloor Mapping of the Atlantic Ocean
Bibliographic record
Abstract
Patricio Bernal, the Coordinator of the International Union for Conservation of Nature High Seas Initiative, once wrote: “We know more about the surface of the Moon and about Mars than we do about the deep seafloor, despite the fact that we have yet to extract a gram of food, a breath of oxygen or a drop of water from those bodies” (Snelgrove, 2010). Often referred to as the last frontier on Earth, the deep seafloor is thought to shelter both critical ecosystems and exploitable resources (i.e., minerals, bio-active natural products, and genetic material, in addition to food resources already being harvested by the fishing industry). These resources are said to have enormous potential to contribute to the growth of the blue economy, potential that will be realized only with an increased understanding of deep-sea environments (Glover et al., 2018). However, knowledge of deep-sea environments and the anthropogenic impacts on them lags in comparison to other marine environments. To address this issue, several cooperative international agreements have been signed. For instance, the Galway Statement (signed by the European Union, United States, and Canada) and the Belém Statement (also signed by Brazil and South Africa) were endorsed to launch an All-Atlantic Ocean Research Alliance. This alliance aims to increase our understanding of the Atlantic Ocean and its systems and promote the sustainable management of its resources. In addition, activities and programs associated with the United Nations Decade of Ocean Science for Sustainable Development (2021–2030), such as The Nippon Foundation-GEBCO Seabed 2030 Project, Challenger 150, and the One Ocean Network for Deep Observation, will likely help increase awareness of the importance of seafloor mapping.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 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".