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Avaliação de impactos de odores em bacias hidrográficas com produções de suínos

2007· article· pt· W1967552347 on OpenAlexaff
Paulo Belli Filho, Glades Pinheiro da Silva, Caroline de Liz Santo, Henrique de Melo Lisboa, Gersina Nobre do Carmo

Bibliographic record

VenueEngenharia Sanitaria e Ambiental · 2007
Typearticle
Languagept
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsHumanitiesPhysicsGeographyPhilosophy

Abstract

fetched live from OpenAlex

A bacia hidrográfica de Rio Fragosos, no oeste catarinense e a bacia hidrográfica do Rio Cachoeirinhas, no sul de Santa Catarina foram unidades pilotos para a aplicação de duas metodologias para avaliações de odores provenientes da suinocultura. Na bacia do Rio Fragosos ficou destacado para a grande maioria das pessoas situações de ambiente desagradável, muito desagradável e extremamente desagradável. As concentrações dos odores obtidos na bacia do Rio Cachoeirinhas, determinadas a montante e a jusante de uma propriedade piloto, no interior de uma granja de criação de animais, sobre uma unidade de armazenamento de seus dejetos e na residência de um produtor variaram de 257 UO/m³à 963 UO/m³. Estas concentrações indicaram intensidades de odores com níveis forte a muito forte.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.279
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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
Published2007
Admission routes1
Has abstractyes

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