Impacts of deep‐sea mining on microbial ecosystem services
Bibliographic record
Abstract
Abstract Interest in extracting mineral resources from the seafloor through deep‐sea mining has accelerated in the past decade, driven by consumer demand for various metals like zinc, cobalt, and rare earth elements. While there are ongoing studies evaluating potential environmental impacts of deep‐sea mining activities, these focus primarily on impacts to animal biodiversity. The microscopic spectrum of seafloor life and the services that this life provides in the deep sea are rarely considered explicitly. In April 2018, scientists met to define the microbial ecosystem services that should be considered when assessing potential impacts of deep‐sea mining, and to provide recommendations for how to evaluate and safeguard these services. Here, we indicate that the potential impacts of mining on microbial ecosystem services in the deep sea vary substantially, from minimal expected impact to loss of services that cannot be remedied by protected area offsets. For example, we (1) describe potential major losses of microbial ecosystem services at active hydrothermal vent habitats impacted by mining, (2) speculate that there could be major ecosystem service degradation at inactive massive sulfide deposits without extensive mitigation efforts, (3) suggest minor impacts to carbon sequestration within manganese nodule fields coupled with potentially important impacts to primary production capacity, and (4) surmise that assessment of impacts to microbial ecosystem services at seamounts with ferromanganese crusts is too poorly understood to be definitive. We conclude by recommending that baseline assessments of microbial diversity, biomass, and, importantly, biogeochemical function need to be considered in environmental impact assessments of deep‐sea mining.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".