Deep sea mineral resources: a true Eldorado? Geological, biological and economic cross-perspectives
Bibliographic record
Abstract
Deep sea minerals (DSM) may be on the verge of a turning point, from the status of a geological resource to that of an economic reserve (according both to the SEC regulation and the ni 43-101 Canadian norm, see http://ccmr-ocrmc.ca/wp-content/uploads/43-101_ni_fr.pdf). Today, this transition notably concerns the potential exploitation of polymetallic nodules in the Clarion-Clipperton Zone.With a view to a possible exploitation of these nodules, the next step, from an economic and financial point of view, has to be based on the realisation of a pre-feasibility study, announced but not carried out to date. The figure of a profitability of 27% is, however, already announced in several documents provided by The Metals Company (TMC) concerning the NORI Area D Mineral Resource project (Technical Report Summary. Initial Assessment of the NORI Property, Clarion-Clipperton Zone, Deep Green Metals Inc., 17 mars 2021).However, the move towards industrial exploitation of deepsea minerals has long been announced, but never been enacted, and it is fraught with various forms of uncertainty. These uncertainties concern the elements of geological, biological and economic knowledge, as well as question the desirability / feasability of this exploitation. Uncertainties also includes the status of the entities and actors involved in the DSM arena. For instance, the categorization of corporations like TMC as junior (speculative) or major (industrial) companies remains an open question, as does the relationship between sponsoring states and partner corporations at ISA.The impacts of the exploitation of DSM on the environment are surely underestimated. The caracterization of the biological components of the ecosystems, including the biological identification and the heterogeneity of the benthic communities and abiotic factors, where the mineral resources are found, but also their relationships with other compartments of the ocean, are very poorly known. Acquiring this knowledge requires further scientific studies, despite international research efforts and APEI (Areas of Particular Environmental Interest) establishments in the CCZ. For example, TMC collected data to study the sedimentary plume generated by a small size pilot nodule collector and concluded that the vertical and lateral extents of this plume are negligible. However, local impacts might still be considerable. In addition, the data that led to this conclusion are not available to the science community for independant validation. Moreover, the impact of the midwater sedimentary plume generated by the ore process (from the surface vessel) is only fewly documentedBecause the geographical distribution of potential deepsea mineral occurences is so large (several tens of thousands of square kms per exploration area) and the available data are so sparse, it is very difficult to assess the lateral extent, continuity and density of nodule fields and Fe-Mn crusts. The spatial heterogeneity is, therefore, likely underestimated and consequently the exploited surface area will be greater than expected and hence the associated impact too.Here, we present the elements gathered within the framework of the IRD collective assessment on the deep-sea knowledge and governance, and question the representation of the deep seabed as a mineral resource Eldorado.
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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 | 0.005 | 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 | 0.005 | 0.012 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".