Mining the ocean floor vs mining the Moon: what can we learn from our past experiences?
Bibliographic record
Abstract
This perspective piece examines the parallels and distinctions between ocean floor mining and potential lunar extraction, emphasizing the necessity of protecting the Moon as a global common. It traces the historical evolution of global commons governance, highlighting key international treaties that have shaped the management of shared resources. The analysis delves into the practical implementation challenges of maintaining equitable access and environmental sustainability in both terrestrial and extraterrestrial contexts. Through a case study of the Pacific Ocean seabed mining initiative by Nauru Ocean Resources Inc., the paper illustrates the complexities and controversies surrounding resource exploitation in recognized global commons. It underscores the inadequacies of current legal frameworks, such as the Moon Agreement and the Law of the Sea, in addressing emerging technological and geopolitical dynamics. The discussion extends to the unique challenges posed by celestial bodies like the Moon and asteroids, advocating for tailored regulatory mechanisms that consider their distinct environmental and regenerative capacities. Last, this perspective piece argues that without just and equitable regulatory decisions in ocean mining, similar oversights are likely in lunar endeavours, thereby jeopardizing the sustainable and fair utilization of outer space resources.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.019 | 0.042 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".