Deep-sea mining risks for sharks, rays, and chimaeras
Bibliographic record
Abstract
Deep-sea mining is expected to cause disturbances of sufficient scale and intensity to pose a risk to biodiversity and ecosystem function. 1 , 2 , 3 We assess the potential impact of deep-sea mining on sharks, rays, and chimaeras in Areas Beyond National Jurisdiction (ABNJ) and identify 30 species (of the total 1,223 marine chondrichthyan species) that overlap spatially with the anticipated mining footprint, specifically through 2 pathways: benthic impacts from physical disturbance and the collector vehicle plume 2 and midwater impacts from the discharge plume. 4 Most species' depth ranges (83%, 25/30, range: 3%–80%) overlapped vertically with the benthic mining footprint, while all species overlapped with discharge plume scenarios. Further, 17 of these species had >50% depth overlap with benthic impacts of at least one of the mineral types. Seven species were egg-laying, benthic, or benthopelagic, which increases their susceptibility to seabed impacts. Filter-feeding species also had high depth overlap with potential midwater discharge plumes. Nearly two-thirds (60%, 18/30) are already threatened with an elevated risk of extinction, and 64.3% are predicted to be threatened. Our analysis raises concerns that deep-sea mining would compound and worsen their extinction risk. We recommend updated risk assessments of significant adverse impacts to chondrichthyans; robust baseline monitoring prior to, during, and after mining; spatial protections near crust and sulfide mining; and that the discharge plume be set at a minimum depth below 2,000 m or at the seabed to minimize overlap with midwater species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".