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Record W2155032597 · doi:10.1002/mnfr.200800296

Arsenic speciation in cattail (<i>Typha latifolia</i>) using chromatography and mass spectrometry

2009· article· en· W2155032597 on OpenAlexaff
Xiufen Lu, Nena Nguyen, Stephan Gabos, X. Chris Le

Bibliographic record

VenueMolecular Nutrition & Food Research · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsAlberta HealthUniversity of Alberta
Fundersnot available
KeywordsArsenicArsenateArseniteGenetic algorithmEnvironmental chemistryChemistryTyphaBotanyBiologyEcology

Abstract

fetched live from OpenAlex

Typha latifolia, commonly known as cattail, is widely used as traditional food and medicinal ingredients by indigenous people. There have been concerns over the high levels of total arsenic in cattail plants, but the chemical species of arsenic in cattail have not been characterized. We describe here the determination of arsenic species in the various compartments of cattail. Average concentrations of total arsenic from 9 to 19 cattail plants were 1120 microg/kg (range 68-2600 microg/kg) in the fine (hairy) roots, 575 microg/kg (range 16-1400) in the skin of tuber, 26 microg/kg (range 2-82) in the core of the tuber, 6 microg/kg (range 5-12) in the stem, and 420 microg/kg (range 4-1970) in the whole tuber. Speciation analysis using strong anion exchange, ion pairing, and strong cation exchange chromatography separation with MS detection revealed the presence of inorganic arsenite, arsenate, dimethylarsinic acid, and monomethylarsonic acid. The two inorganic arsenic species accounted for >80% of the total arsenic. Further analyses of arsenic and iron concentrations showed a strong correlation between arsenic and iron in the fine roots and skin. These results suggest that arsenic and iron are colocalized (codeposited) in the skin of the cattail plants, consistent with the previous findings. The level of exposure to arsenic from the use of cattail as food and medicine can be substantially reduced by removing the skin of cattail.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.304
Teacher spread0.277 · 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 designBench or experimental
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

Citations15
Published2009
Admission routes1
Has abstractyes

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