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Record W2529081072 · doi:10.5382/rev.18.11

The Minor Element Endowment of Modern Sea-Floor Massive Sulfides and Comparison with Deposits Hosted in Ancient Volcanic Successions

2016· book-chapter· en· W2529081072 on OpenAlexaff
Thomas Monecke, Sven Petersen, Mark D. Hannington, H.L.J. Grant, Iain M. Samson

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsUniversity of WindsorUniversity of Ottawa
Fundersnot available
KeywordsCommodityElement (criminal law)GeologyEarth scienceGeochemistryNatural resource economicsPolitical scienceBusinessEconomicsLaw

Abstract

fetched live from OpenAlex

Abstract Sea-floor massive sulfide deposits represent a new type of base and precious metal resources that may be exploited by future deep-sea mining operations. These deposits occur in diverse tectonic environments and are mostly located along the global mid-ocean ridge system within international waters and arc-related settings within the exclusive economic zones of the world’s oceans. Much controversy is currently centered on the question whether sea-floor massive sulfide deposits represent a significant resource of metals that could be exploited to meet the metal demand of modern technology-based society. Chemical analysis of sulfide samples from sea-floor hydrothermal vent sites worldwide shows that sea-floor massive sulfides can be enriched in the minor elements Bi, Cd, Ga, Ge, Hg, In, Mo, Sb, Se, Te, and Tl, with concentrations ranging up to several tens or hundreds of parts per million. The minor element content of seafloor sulfides broadly varies with volcanic and tectonic setting. Massive sulfides on mid-ocean ridges commonly show high concentrations of Se, Mo, and Te, whereas arc-related sulfide deposits can be enriched in Cd, Hg, Sb, and Tl. Superposed on the volcanic and tectonic controls, the minor element content of sea-floor sulfides is strongly influenced by the temperature-dependent solubility of these elements. The high- to intermediatetemperature suite of minor elements, Bi, In, Mo, Se, and Te, is typically enriched in massive sulfides composed of chalcopyrite, while the low-temperature suite of minor elements, Cd, Ga, Ge, Hg, Sb, and Tl, is more typically associated with sphalerite-rich massive sulfides. Temperature-related minor element enrichment trends observed in modern sea-floor hydrothermal systems are broadly comparable to those encountered in fossil massive sulfide deposits. Although knowledge on the mineralogical sequestration of the minor elements in sea-floor massive sulfide deposits is limited, a significant proportion of the total amount of minor elements contained in massive sulfides appears to be incorporated into the crystal structure of the main sulfide minerals, including pyrite, pyrrhotite, chalcopyrite, sphalerite, wurtzite, and galena. In addition, the over 80 trace minerals recognized represent important hosts of minor elements in massive sulfides. As modern sea-floor sulfides have not been affected by metamorphic recrystallization and remobilization, the minor element distribution and geometallurgical properties of the massive sulfides may differ from those of ancient massive sulfide deposits. The compilation of geochemical data from samples collected from hydrothermal vent sites worldwide now permits a first-order evaluation of the global minor element endowment of sea-floor sulfide deposits. Based on an estimated 600 million metric tons (Mt) of massive sulfides in the neovolcanic zones of the world’s oceans, the amount of minor elements contained in sea-floor deposits is fairly small when compared to land-based mineral resources. Although some of the minor elements are potentially valuable commodities and could be recovered as co- or by-products from sulfide concentrates, sea-floor massive sulfide deposits clearly do not represent a significant or strategic future resource for these elements.

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 categoriesInsufficient 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: none
Teacher disagreement score0.465
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.201
Teacher spread0.191 · 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

Citations66
Published2016
Admission routes1
Has abstractyes

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