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Os determinantes da inclusão digital

2005· dissertation· en· W33887502 on OpenAlexfundno aff
Alexandre Toledo do Lago Leite

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicEducation and Digital Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsComputer science

Abstract

fetched live from OpenAlex

Benzophenones are widely used as organic UV filters in many personal care products, especially sunscreen, to protect humans from UV radiation. The increasing use of benzophenone class UV filters has raised concerns about the potential effects on the aquatic environment. These organic UV filters are designed to absorb UV light. However, to date, studies have not considered the potential of UV light to potentiate the toxicity of benzophenones in aquatic organisms. In this study using zebrafish embryos, we assessed the median lethal concentration (LC<sub>50</sub>) and sub-lethal effects of benzophenone and oxybenzone either under natural levels of UV light or under laboratory light conditions. The LC<sub>50</sub> value in zebrafish embryos under both light conditions of oxybenzone was lower when compared to benzophenone. Interestingly, UV light significantly decreased the LC<sub>50</sub> values (increased toxicity) of both benzophenone and oxybenzone. The presence of UV light induced a significant increase in hydroxyl radical formation and this was reflected in both increased SOD activity and lipid peroxidation in oxybenzone treated groups. Exposure to either benzophenone or oxybenzone also delayed hatching between 60 and 96 hpf when comparing to the control group while UV exposure further delayed hatching only in oxybenzone-exposed embryos. The results demonstrate the importance of involving UV light in toxicity testing for UV filters and provide much-need information on the UV-induced toxicity of benzophenone and oxybenzone under ecologically realistic conditions.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.806
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.028
GPT teacher head0.376
Teacher spread0.348 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2005
Admission routes1
Has abstractyes

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