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Record W4388496286 · doi:10.1038/s44183-023-00030-w

To engage in deep-sea mining or not to engage: what do full net cost analyses tell us?

2023· article· en· W4388496286 on OpenAlex
U. Rashid Sumaila, Lawrence Alam, Kumara Perumal Pradhoshini, Temitope T. Onifade, S. Karuaihe, Paul Singh, Lisa A. Levin, R. Flint

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenpj Ocean Sustainability · 2023
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
Fundersnot available
KeywordsFoundation (evidence)SeabedWork (physics)Net (polyhedron)Computer scienceData scienceOperations researchEngineeringPolitical scienceOceanographyGeologyLawMathematics

Abstract

fetched live from OpenAlex

Deep-sea mining (DSM)—the extraction of minerals from the deep seafloor, currently focused intensively on the abyssal plains of the Pacific Ocean—has attracted the attention of mining companies, investors, non-governmental organizations (NGOs), governments, scientists, and the public at large, for good reason 1 . The International Seabed Authority (ISA), an intergovernmental organization established under Article 156 of the United Nations Convention on the Law of the Sea (UNCLOS), is the primary body regulating the exploration and exploitation of minerals found on the international seafloor, termed the Area. These minerals are the common heritage of humankind under UNCLOS, and ISA is entrusted to ensure that mining activities are to be carried out for the benefit of humankind as a whole 2 . As a global platform for states to organize and control activities in the international seabed, ISA’s role in resolving DSM-related issues is very important.

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.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.316
Teacher spread0.281 · 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