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Record W1982268248 · doi:10.1139/s05-006

Aqueous phase arsenic in weathered shale enriched in native arsenic

2005· article· en· W1982268248 on OpenAlexvenueno aff
Tracy Daniel. Muloin, M. J. Dudas

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsArsenicOil shaleArsenateEnvironmental chemistryChemistryAdsorptionWeatheringDesorptionSulfideArseniteSulfide mineralsIron sulfidePyriteGeologySulfurMineralogyGeochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Soils and geologic deposits derived from marine shale often contain elevated levels of arsenic. The source of arsenic is iron sulfide minerals. Oxidative weathering breaks down the iron sulfide minerals and potentially releases undesirable levels of arsenic into the surrounding pore water. Equilibration experiments on a weathered marine shale containing naturally elevated levels of native arsenic and on a mixture of weathered shale and calcareous till were conducted under aerobic conditions to determine how much arsenic was released into pore water and the mechanism responsible for limiting soluble arsenic concentrations. Aqueous phase arsenic released from the shale was less than 10 µg/L for aerobic conditions. Soluble arsenic concentrations were low considering the high total As concentrations in the shale (30 µg As/g shale). Over 4 times as much solution arsenic was released from the higher pH shale–till mixture compared to the acid shale alone. Adsorption and desorption was the mechanism that controlled the concentration of aqueous phase arsenic. Key words: soluble arsenate, groundwater, solution chemistry, adsorption, background concentration.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.004
GPT teacher head0.212
Teacher spread0.208 · 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 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

Citations9
Published2005
Admission routes1
Has abstractyes

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