Beyond the plains: deep-sea mining of polymetallic nodules on and around seamounts
Bibliographic record
Abstract
Deep-sea mining management, scientific research, and public discourse have largely focused on polymetallic nodule extraction from abyssal plains. However, there is growing commercial interest in nodules on and around seamounts, with exploration and testing underway in the Pacific Ocean. Increasing documentation of nodules-seamount habitats and co-occurrence with cobalt-rich ferromanganese crusts refutes the misconception that nodules occur only in abyssal plains. This also challenges the conventional management framework that separates these mineral resources into distinctly different habitats. Nodule exploitation is poised to begin soon in both environments, but under the rubric developed for abyssal plains alone. Existing and developing guidance based on the simplified resource-habitat framework is likely inadequate in addressing where nodule fields are associated with seamounts. Seamounts are ecologically significant and vulnerable features, often linked to islands as part of volcanic chains, and embedded in dynamic oceanographic systems that can amplify mining impacts. Sustainable management will require an integrated and adaptive approach, including critical reassessment of Regional Environmental Management Plans in international waters and complementary frameworks in national waters, as nodule mining moves beyond abyssal plains and onto seamounts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".