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Record W7005635819

Requisitos legais e critérios de ecotoxicidade dos efluentes no Brasil

2017· other· pt· W7005635819 on OpenAlexfundno aff

Bibliographic record

VenueUFPR Digital Collection (Federal University of Paraná) · 2017
Typeother
Languagept
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of CanadaEgg Farmers of Canada
KeywordsGovernment (linguistics)LegislationWork (physics)Public policy
DOInot available

Abstract

fetched live from OpenAlex

Knowledge about the impacts caused due to the disposal of wastewater is essential to ensure the quality of aquatic biomes.Effluents are composed of a wide variety and quantity of substances that make purely physical and chemical environmental control insufficient for toxic effects in the environment to be checked.That is the reason why the study of Ecotoxicity emerged, observing the interaction of the effluent with the biological environment.The most commonly tests performed assess the acute (short-time) and chronic (longer exposure period) impacts on organisms such as algae, invertebrates and vertebrates with effluent dilutions.This piece of work aimed at analyzing the Brazilian and the international regulations that discipline on the Ecotoxicity theme and also make comparisons.In the federal sphere of Brazil, the resolution made by CONAMA under number 430/2011 regulates the guidelines to be evaluated in effluents, and only five Brazilian states have specific rules, although the state of Rio Grande do Sul has recently revoked its normative.It is observed that the regulations of the Brazilian states have different restrictions for the same river grade.Furthermore, some states specify the species to be used in the test, which can generate results in the study of ecosystem that are actually not representative.It is clear that all Brazilian regulations have been based on American or European regulations.Therefore the development of studies and the knowledge on this subject should be frequent and as such the Brazilian regulation should also be periodically evaluated so that it does not become obsolete or incoherent with their assessment.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.271
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0500.004

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 head0.208
Teacher spread0.198 · 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; both teacher heads agree on what is shown here.

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
Published2017
Admission routes1
Has abstractyes

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