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Mercury content in mining waste and its effect on the surrounding environment (Case study: Small-scale gold mining locations-West Sumbawa)

2023· article· en· W4382775764 on OpenAlexaboutno aff
Wahyu Garinas, H Hidayaturahman, Asep Nurohmat Majalis

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
Fundersnot available
KeywordsMercury (programming language)Gold miningSedimentWatershedEnvironmental scienceWater qualityWastewaterMining engineeringHeavy metalsEnvironmental chemistryEnvironmental engineeringGeologyChemistryEcology

Abstract

fetched live from OpenAlex

Abstract This study was conducted to determine the content of mercury in the flowing river around small-scale gold mines. Mining wastes from the run of mining are dumped into the watersheds and potentially contaminate the water and sediment in the watershed near the gold mining area. We analyzed the characteristics of the waste and the result was compared with the water quality standards (Environment Minister Decision No.202 of 2004 and Government Regulation No.82 of 2001) and sediment quality standard (the Canadian sediment quality guidelines for the threshold effect level (TEL) and probable effect level (PEL)). We found that wastewater from the gold mining areas was categorized in class IV. The mercury content in the sediment samples did not meet the standards of Canadian sediment quality guidelines for TEL. The content of mercury in the sediment samples from the gold mine site was very high. The sediment mercury samples had been settled for a long time around the watershed. The content of mercury in the mining location indicated that mercury had been contaminating the area around the small-scale gold mining.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.054
GPT teacher head0.251
Teacher spread0.197 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2023
Admission routes1
Has abstractyes

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