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Record W4286544420 · doi:10.21577/0103-5053.20220102

When the Detail of Organism Makes the Difference in the Seascape: Different Tissues of Phallusia nigra Have Distinct Hg Concentrations and Show Differences Resolution in Spatial Pollution

2022· article· en· W4286544420 on OpenAlexaff
Sabrina Teixeira Martinez, Caio S.A. Felix, Rayane Sorrentino, Igor Cristino Silva Cruz, Jaílson B. de Andrade

Bibliographic record

VenueJournal of the Brazilian Chemical Society · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsDiscovery Air (Canada)
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoMinistério da Ciência, Tecnologia e InovaçãoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsBayPollutionHepatopancreasMercury (programming language)BioaccumulationSeascapeBioindicatorEnvironmental chemistryMercury contaminationBiologyContaminationZoologyChemistryFisheryEcologyOceanographyGeology

Abstract

fetched live from OpenAlex

This study indicates the use of Phallusia nigra as a potential biomonitor of mercury contamination. In this way, Hg levels were measured in seawater and different parts of ascidians (tunic, hepatopancreas, and branchial basket) from eight different sites in the Todos os Santos Bay, Salvador-Bahia. The ascidians were lyophilized, weighed, and taken to the DMA-80 (direct mercury analyzer); the method accuracy was confirmed by analyzing the certified material DORM- 4 muscle tissue and MEES-3 marine sediment with a confidence level of 95%. The results were evaluated through the Tukey’s test and it was possible to observe a higher concentration of Hg (82.00-312.7 ng g-1) in the branchial basket, followed by the hepatopancreas (69.67‑130.7 ng g-1) and tunic (21.63-33.27 ng g-1). Thus, the branchial basket was the only tissue capable of identifying spatial differences in pollution between the points.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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