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Record W1985307351 · doi:10.1021/es900086r

Arsenic Speciation of Terrestrial Invertebrates

2009· article· en· W1985307351 on OpenAlexafffundabout
Maeve M. Moriarty, Iris Koch, R. A. Gordon, Kenneth J. Reimer

Bibliographic record

VenueEnvironmental Science & Technology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsRoyal Military College of CanadaSimon Fraser University
FundersNational Research Council Canada
KeywordsArsenobetaineArsenicEnvironmental chemistryArsenateGenetic algorithmMetalloidArseniteInvertebrateChemistryInductively coupled plasma mass spectrometryBiologyEcologyMass spectrometryChromatography

Abstract

fetched live from OpenAlex

The distribution and chemical form (speciation) of arsenic in terrestrial food chains determines both the amount of arsenic available to higher organisms, and the toxicity of this metalloid in affected ecosystems. Invertebrates are part of complex terrestrial food webs. This paper provides arsenic concentrations and arsenic speciation profiles for eight orders of terrestrial invertebrates collected at three historical gold mine sites and one background site in Nova Scotia, Canada. Total arsenic concentrations, determined by inductively coupled plasma mass spectrometry (ICP-MS), were dependent upon the classification of invertebrate. Arsenic species were determined by high-performance liquid chromatography (HPLC) ICP-MS and X-ray absorption spectroscopy (XAS). Invertebrates were found by HPLC ICP-MS to contain predominantly arsenite and arsenate in methanol/water extracts, while XAS revealed that most arsenic is bound to sulfur in vivo. Examination of the spatial distribution of arsenic within an ant tissue highlighted the differences between exogenous and endogenous arsenic, as well as the extent to which arsenic is transformed upon ingestion. Similar arsenic speciation patterns for invertebrate groups were observed across sites. Trace amounts of arsenobetaine and arsenocholine were identified in slugs, ants, and spiders.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.457
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.005
GPT teacher head0.206
Teacher spread0.201 · 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 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

Citations82
Published2009
Admission routes3
Has abstractyes

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