Implications of deep-seabed mining on marine ecosystems—Introduction to a special series of papers
Bibliographic record
Abstract
Abstract After more than 50 years of exploration and research that has intensified over the past decade, deep-seabed mining (DSM) remains a controversial subject, resulting mainly from legacy issues of other extractive industries. Moreover, our planet is environmentally challenged, with climate change as one of the major issues that we collectively face. Deep-seabed mining aims to collect metal resources lying on the deep seabed to help meet increased global demand caused by growth in population and urbanization, and clean energy, in a way that reduces pressures on land, such as deforestation and community relocation. The metals found on the seabed are those needed to address climate change through clean energy technologies. An important question facing us is: How do we, most responsibly, obtain the metals we need with the least impact on the planet we are trying to protect? DSM is one of the options to meet the demand. In this IEAM special series, we set out to present neutral and unbiased perspectives on the environmental implications of DSM. Our aim is to offer readers environmental management considerations learned by researchers around the world and working in diverse aspects of the field, including: population and community assessment, biota ecosystem services, environmental ethics, and rehabilitation and restoration. In consideration of the controversies, fundamental questions still remain: How can a new industry be given the opportunity to “do the right thing”? How do we make evidence-based decisions about where metals should come from when emotions and possibly fear often seem to drive the debate? Can we assume that decisions and policies are best achieved based on data and evidence? The papers presented in the series help address these questions and cover a range of diverse topics from ethical frameworks to biodiversity assessment to risk assessment to restoration. Integr Environ Assess Manag 2022;18:631–633. © 2021 SETAC KEY POINTS Deep seabed mining (DSM) is one of the options to meet the demand for metals globally. This special series presents neutral and unbiased perspectives on the environmental implications of DSM. Fundamental questions remain: can a new industry be given the opportunity to “do the right thing”; how do evidence-based decisions about where metals should come from be made, when emotions and fear often drive the debate, and can we assume that decisions and policies are best achieved based on data and evidence. Papers presented in the special series help address key questions and cover a range of diverse topics from ethical frameworks to biodiversity assessment to risk assessment to restoration.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".