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Record W2057455169 · doi:10.1080/10934521003648958

The effect of residence time and fluid volume to soil mass (LS) ratio on<i>in vitro</i>arsenic bioaccessibility from poorly crystalline scorodite

2010· article· en· W2057455169 on OpenAlexafffund
Brian Laird, Derek Peak, Steven D. Siciliano

Bibliographic record

VenueJournal of Environmental Science and Health Part A · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsArsenicArsenateArseniteChemistryDissolutionEnvironmental chemistryMicroorganismAntimonySaturation (graph theory)BioavailabilityResidence time (fluid dynamics)Inorganic chemistryBiologyPharmacologyGeologyBacteria

Abstract

fetched live from OpenAlex

Percent arsenic bioaccessibility is occasionally dependent upon arsenic concentration; however, the mechanism(s) of this relationship has not yet been defined. To evaluate the mechanism of this relationship, the arsenic bioaccessibility from freshly synthesized poorly crystalline scorodite was measured in the stomach, small intestine, and colon stages of the Simulator of the Human Intestinal Microbial Ecosystem (SHIME). The shape of the arsenic dissolution isotherms were different between stages (stomach: linear; small intestine: exponential rise to maxima; colon: sigmoidal). These results indicate that arsenic bioaccessibility may be limited by either in vitro GI fluid saturation or in vitro GI model residence time, depending upon the chemical/microbiological conditions of the model. Gastrointestinal microorganisms increased arsenic bioaccessibility of scorodite up to two-fold in the SHIME colon; however, this was dependent upon the sample arsenic concentration. Up to 40% of the bioaccessible arsenic was reduced to arsenite; however this process was neither mediated by GI microorganisms nor associated with increased arsenic bioaccessibility.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.259
Teacher spread0.252 · 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 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

Citations21
Published2010
Admission routes2
Has abstractyes

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