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Record W1502604500 · doi:10.3133/sir20105070c

Volcanogenic massive sulfide occurrence model

2012· article· en· W1502604500 on OpenAlexaboutno aff
Wayne C. Shanks, Randolph A. Koski, Dan L. Mosier, Klaus J. Schulz, Lisa A. Morgan, John F. Slack, W. I. Ridley, Cynthia Dusel-Bacon, Robert R. Seal, Nadine M. Piatak

Bibliographic record

VenueScientific investigations report · 2012
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsSeafloor spreadingGeologyVolcanogenic massive sulfide ore depositGeochemistrySulfideVolcanoHydrothermal circulationPyritePaleontology

Abstract

fetched live from OpenAlex

Volcanogenic massive sulfide deposits, also known as volcanic-hosted massive sulfide, volcanic-associated massive sulfide, or seafloor massive sulfide deposits, are important sources of copper, zinc, lead, gold, and silver (Cu, Zn, Pb, Au, and Ag). These deposits form at or near the seafloor where circulating hydrothermal fluids driven by magmatic heat are quenched through mixing with bottom waters or porewaters in near-seafloor lithologies. Massive sulfide lenses vary widely in shape and size and may be podlike or sheetlike. They are generally stratiform and may occur as multiple lenses. Volcanogenic massive sulfide deposits range in size from small pods of less than a ton (which are commonly scattered through prospective terrains) to supergiant accumulations like Rio Tinto (Spain), 1.5 billion metric tons; Kholodrina (Russia), 300 million metric tons; Windy Craggy (Canada), 300 million metric tons; Brunswick No. 12 (Canada), 230 million metric tons; and Ducktown (United States), 163 million metric tons. Volcanogenic massive sulfide deposits range in age from 3.55 billion years to zero-age deposits that are actively forming in extensional settings on the seafloor, especially mid-ocean ridges, island arcs, and back-arc spreading basins. The widespread recognition of modern seafloor Volcanogenic massive sulfide deposits and associated hydrothermal vent fluids and vent fauna has been one of the most astonishing discoveries in the last 50 years, and seafloor exploration and scientific studies have contributed much to our understanding of ore-forming processes and the tectonic framework for volcanogenic massive sulfide deposits in the marine environment. Massive ore in volcanogenic massive sulfide deposits consists of greater than 40 percent sulfides, usually pyrite, pyrrhotite, chalcopyrite, sphalerite, and galena; non-sulfide gangue typically consists of quartz, barite, anhydrite, iron oxides, chlorite, sericite, talc, and their metamorphosed equivalents. Ore composition may be Pb-Zn-, Cu-Zn-, or Pb-Cu-Zn-dominated, and some deposits are zoned vertically and laterally. Many deposits have stringer or feeder zones beneath the massive zone that consist of crosscutting veins and veinlets of sulfides in a matrix of pervasively altered host rock and gangue. Alteration zonation in the host rocks surrounding the deposits are usually well-developed and include advanced argillic (kaolinite, alunite), argillic (illite, sericite), sericitic (sericite, quartz), chloritic (chlorite, quartz), and propylitic (carbonate, epidote, chlorite) types. An unusual feature of VMS deposits is the common association of stratiform "exhalative" deposits precipitated from hydrothermal fluids emanating into bottom waters. These deposits may extend well beyond the margins of massive sulfide and are typically composed of silica, iron, and manganese oxides, carbonates, sulfates, sulfides, and tourmaline.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0410.007

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 head0.043
GPT teacher head0.260
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations93
Published2012
Admission routes1
Has abstractyes

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