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Record W2982259742 · doi:10.4095/214977

Transport and attenuation of arsenic, cobalt and nickel in an alkaline environment (Cobalt, Ontario)

2004· report· en· W2982259742 on OpenAlexaffabout
J B Percival, Y. T. John Kwong, C G Dumaresq, F A Michel

Bibliographic record

Venuenot available
Typereport
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsCobaltArsenicNickelCobalt extraction techniquesEnvironmental chemistryEnvironmental scienceChemistryMetallurgyInorganic chemistryMaterials science

Abstract

fetched live from OpenAlex

Understanding the behaviour of arsenic in natural waters is important because arsenic and its compounds are toxic to humans and aquatic organisms. The Cobalt area, once renowned for its bonanza silver deposits, is now characterized by numerous deposits of arsenic-rich tailings, waste rock piles and remnant historic mine workings. Very limited mitigation has been undertaken since the cessation of mining. Thus, despite its slightly alkaline character, the surface drainage system continues to be contaminated from leaching of the widespread mine wastes. This Open File documents the transport and attenuation of arsenic and associated elements in the surficial environment from mine wastes (tailings and waste rock) through surface waters to wetlands of the Farr Creek drainage basin in Cobalt, Ontario.

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 categoriesMeta-epidemiology (narrow), Insufficient 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: Empirical
Teacher disagreement score0.397
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.299
Teacher spread0.251 · 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

Citations16
Published2004
Admission routes2
Has abstractyes

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