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Record W2319449889 · doi:10.5670/oceanog.2012.54

Gold Mining and Submarine Tailings Disposal: Review and Case Study

2012· article· en· W2319449889 on OpenAlex

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOceanography · 2012
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversitas Sam Ratulangi
KeywordsTailingsSubmarineMining engineeringGeologyEnvironmental scienceOceanographyMetallurgy

Abstract

fetched live from OpenAlex

Environmental impacts associated with submarine tailings disposal (STD) of gold mine wastes vary widely among the relatively few cases studied. The principal contaminants of concern surrounding most gold mines are arsenic, mercury, and cyanide, although antimony, thallium, lead, zinc, and copper may also be important in particular mines. The mineralogy and ore processing techniques associated with different kinds of gold deposits may strongly influence the outcome of STD. Native gold and its associated minerals are generally less toxic than sulfide-mineral gold, in which the gold is incorporated into sulfide minerals in conjunction with other trace elements. Sulfide gold tailings placed in seawater may be particularly dangerous where ore processing includes oxidation by roasting or aggressive chemical leaching, which transforms the sulfide minerals into relatively unstable oxides and oxy-hydroxides.The case study of the Newmont Minahasa Raya gold mine in Indonesia highlights some of the dangers of gold mine STD. Local villagers observed fish kills shortly after the beginning of STD operations, and they also noted fine red sediment resembling the tailings smothering corals on reefs adjacent to the tailings disposal site. Tailings from this mine dispersed from the intended STD depth of 82 m up to nearby coral reefs, and dispersal extended up to 3.5 km from the end of pipe. Unstable arsenic phases in the tailings accounted for at least 32% of total arsenic in the mine tailings, and less than 10% of total arsenic in fluvially derived marine sediments. Mercury in the submarine tailings was methylated in approximately the same proportions as mercury from artisanal gold mines using mercury amalgamation and in uncontaminated nearshore marine sediments near a watershed with similar bedrock geology. Methyl mercury derived from tailings was incorporated into the local food chain, probably via benthic invertebrates.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score0.507

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)0.0000.000

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 head0.014
GPT teacher head0.220
Teacher spread0.206 · 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