Land and deep-sea mining: the challenges of comparing biodiversity impacts
Bibliographic record
Abstract
Abstract The term ‘biodiversity,’ while casually used in practice, is a complicated subject to measure, interpret, contextualize, and compare. Yet the possible advent of deep-sea mining in the mid-2020’s compels us to compare potential impacts of biodiversity loss across ecologically distant realms, a formidable task. Supplying the world’s green infrastructure is expected to lead to shortages of nickel, cobalt and other metals; meanwhile polymetallic nodules sitting atop the abyssal plains of the Clarion-Clipperton Zone (CCZ) of the Pacific Ocean contain billions of tons of nickel, cobalt, copper and manganese, enough to solve the supply issues. Implicit in society’s decision of whether to exploit this resource is a tradeoff of harm to biodiversity in the CCZ’s abyssal seafloor and its overlying water column, versus intensification of harm to rainforests and other terrestrial mining habitats. Here we frame the challenges of comparing biodiversity impacts across such different realms, spanning the gamut from normative to fundamental: ambiguities in definitions, lack of protocol standardization, physical challenges in measurement, difficulties to integrate measures among different taxonomic groups, profound differences between ecologically distant realms, contextual necessity to attribute value to mathematical index results, and constraints of current knowledge about species, ecosystems and system level impacts of biodiversity change. Quantitative biodiversity measures alone cannot rank one system above the other; measures must be supplemented with qualitative judgements of the tangible and intangible values of species and habitats to natural systems and to humans, along with consideration of other threats that they and we face.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".