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IMPACTOS PROVOCADOS PELO DESCARTE DO Limnoperna fortunei EM PISCICULTURAS DO SUB-MÉDIO RIO SÃO FRANCISCO

2019· article· pt· W4236881808 on OpenAlexaff
Patrick Gomes Avelino, Danielle Ferreira Gomes Avelino, Tâmara de Almeida e Silva

Bibliographic record

VenueRevista Interfaces Saúde Humanas e Tecnologia · 2019
Typearticle
Languagept
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsImpact
Fundersnot available
KeywordsEnvironmental science

Abstract

fetched live from OpenAlex

In order to verify the impacts, final disposal and disposal of shells removed in the cleaning of cultivation structures in six fish farms in Jatobá-PE were observed in loco the forms of disposal that are performed by fish farmers also as well the due impacts from the shells of the golden mussel Limnoperna fortunei (Dunker, 1856).These observations it happen in two distinct periods dry and rainy periods of 2018.The Jatobá-PE fish farmers ended up suffering economic impacts due to the increase in investment that was required to repair the structures that suffered damage in the act of removing the encrusted shells which may also cause impacts to the which may also cause environmental impacts if the due proper final disposal not happen it.Given the observations it was found that fish farmers use different methodologies at the time of disposal, which may or may not impact the environment that these shells are discarded.Given the importance of aquaculture production in the region and the impacts that it itself can suffer due to the unwanted presence of the golden mussel, harming the species to be cultivated.With this in mind, work on this theme is important to facilitate the resolution of problems related to the invasive mussel that other producers face in their daily lives, causing a decrease in their production.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.016

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 head0.014
GPT teacher head0.240
Teacher spread0.225 · 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 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

Citations1
Published2019
Admission routes1
Has abstractyes

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