MétaCan
Menu
Back to cohort
Record W1979198691 · doi:10.1021/es052559a

Localizing the Biochemical Transformations of Arsenate in a Hyperaccumulating Fern

2006· article· en· W1979198691 on OpenAlexaff
Ingrid J. Pickering, Luke Gumaelius, Hugh H. Harris, Roger C. Prince, Gregory Hirsch, Jo Ann Banks, David E. Salt, Graham N. George

Bibliographic record

VenueEnvironmental Science & Technology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsExxonMobil (Canada)University of Saskatchewan
Fundersnot available
KeywordsFernArsenicArsenatePteris vittataArseniteFrondX-ray absorption spectroscopyBotanyChemistryVacuoleBiologyEnvironmental chemistryHyperaccumulatorBiochemistryAbsorption spectroscopyHeavy metalsPhytoremediation

Abstract

fetched live from OpenAlex

The fern Pteris vittata accumulates unusually high levels of arsenic. Using X-ray absorption spectroscopy (XAS) and XAS imaging, we reveal the distribution of arsenic species in vivo. Arsenate is transported through the vascular tissue from the roots to the fronds (leaves), where it is reduced to arsenite and stored at high concentrations. Arsenic-thiolate species surrounding veins may be intermediates in this reduction. In gametophytes, arsenite is compartmentalized within the cell vacuole. Arsenic is excluded from cell walls, rhizoids, and reproductive areas. This study provides important insights into arsenic hyperaccumulation, which may prove useful for phytoremediating arsenic-contaminated sites, and demonstrates the strengths of XAS imaging for distinguishing highly localized species.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

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 head0.207
Teacher spread0.203 · 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 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

Citations207
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental Science & TechnologySame topicArsenic contamination and mitigationFrench-language works237,207