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Record W2020946023 · doi:10.1002/aoc.269

Determination of arsenic species in a freshwater crustacean <i>Procambarus clarkii</i>

2002· article· en· W2020946023 on OpenAlexaff
Vicenta Devesa, M. A. Súñer, Vivian Lai, S. C. R. Granchinho, José M. Martínez, Dinoraz Vélez, Walter Cullen, R. Montoro

Bibliographic record

VenueApplied Organometallic Chemistry · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsUniversity of British Columbia
FundersEuropean Regional Development FundMinisterio de Educación y CulturaGeneralitat Valenciana
KeywordsArsenicProcambarus clarkiiArsenobetaineCrayfishChemistryEnvironmental chemistryInorganic arsenicCrustaceanAnimal scienceEcologyBiology

Abstract

fetched live from OpenAlex

Abstract The arsenic species present in samples of the crayfish Procambarus clarkii caught in the area affected by the toxic mine‐tailing spill at Aznalcóllar (Seville, Southern Spain) were analyzed. The total arsenic contents ranged between 1.2 and 8.5 µg g −1 dry mass (DM). With regard to the different species of arsenic, the highest concentrations were for inorganic arsenic (0.34–5.4 µg g −1 DM), whereas arsenobetaine, unlike the situation found in marine fish products, was not the major arsenic species (0.16 ± 0.09 µg g −1 DM). Smaller concentrations were found of arsenosugars 1a (0.18 ± 0.11 µg g −1 DM), 1b (0.077 ± 0.049 µg g −1 DM), 1c (0.080 ± 0.089 µg g −1 DM), and 1d (0.14 ± 0.13 µg g −1 DM). The presence of two unknown arsenic species was revealed (U1: 0.058 ± 0.058 µg g −1 DM; U2: 0.12 ± 0.12 µg g −1 DM). No significant differences were seen with respect to the total arsenic contents between the sexes. However, significant differences in the total arsenic contents were revealed between the area affected by the spill and the area not affected, the contents being greater in the affected area. Copyright © 2002 John Wiley &amp; Sons, Ltd.

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 score0.117
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0700.001

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 head0.010
GPT teacher head0.182
Teacher spread0.171 · 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

Citations39
Published2002
Admission routes1
Has abstractyes

Explore more

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