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Record W4408823247 · doi:10.5194/oos2025-1239

Deep sea mineral resources: a true Eldorado? Geological, biological and economic cross-perspectives

2025· preprint· en· W4408823247 on OpenAlexaboutno aff
Julien Collot, Vincent Géronimi, W. R. Roest, Sarah Samadi, Stéphane Goutte, Karine Olu, Valelia Muni Toke, Anouk Barberousse, Pierre-Yves Lemeur

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMineral resource classificationGeologyEarth scienceMining engineeringNatural resource economicsGeochemistryEconomics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.022
GPT teacher head0.239
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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