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Record W1992058063 · doi:10.1080/02772240802541387

Arsenic in Ironite fertilizer: The absorption by hamsters and the chemical form

2009· article· en· W1992058063 on OpenAlexaff
Mary M. Aposhian, Iris Koch, Mihaela D. Avram, Uttam Kumar Chowdhury, Paula G. Smith, Ken Reimer, H. Vasken Aposhian

Bibliographic record

VenueToxicological & Environmental Chemistry Reviews · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsArsenicAbsorption (acoustics)Environmental chemistrySodium arseniteChemistryFertilizerArseniteAtomic absorption spectroscopyMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

We determined the gastrointestinal absorption of the arsenic in Ironite, a readily available fertilizer, for male hamsters (Golden Syrian), considered to be an excellent model for how the human processes inorganic arsenic. Urine and feces were collected after administering an aqueous suspension of Ironite by stomach tube. In addition, we studied the forms and oxidation states of arsenic in Ironite by synchrotron spectroscopic techniques. The absorption of the arsenic in Ironite (1-0-0) was 21.2% and the absorption relative to sodium arsenite was 31.0%. Our results using XANES spectra determinations indicate that Ironite contains scorodite (AsV) as well as previously reported arsenopyrite (As(−1)). Since the 1-0-0 Ironite is readily available for purchase, its risk assessment for children by professionals is recommended. This is especially important because it is used to fertilize large areas of grass in playgrounds and parks where children play. The absorption of the arsenic in it, the hand to mouth activity of children, and the potential of ground water contamination makes the use of 1-0-0 Ironite as a fertilizer a potential environmental hazard.

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 score0.001
Threshold uncertainty score0.003

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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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