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Record W4389170649 · doi:10.18066/inic0267.23

CONDUTIVIDADE ELÉTRICA APARENTE DO SOLO EM ÁREAS DE PASTAGEM CULTIVADAS SOB DIFERENTES MANEJOS

2023· article· pt· W4389170649 on OpenAlexaff
Thiago Alexandre da Silva Oliveira, Paula Alberti Bonadiman, Amanda Teixeira Moreira Capacia, João Mendes Cicari Hott, Isaias Brinati Valentim, Samuel de Assis Silva

Bibliographic record

Venuenot available
Typearticle
Languagept
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsPhysicsEnvironmental science

Abstract

fetched live from OpenAlex

Com este trabalho se objetivou avaliar a influência das práticas de manejo sobre a CEa, e avaliar a correlação entre teor de água no solo e CEa.O estudo foi realizado no município de Alegre-ES, em duas áreas planas cultivadas com capim-tanzânia (Panicum maximum cv.Tanzânia), sob diferentes manejos, em uma área será realizado o manejo de cobertura e na outra não será aplicado nenhum tipo de fertilizante, representando os cultivos locais.O levantamento dos dados foi feito em uma grid amostral irregular, distribuídos em duas áreas (A e B), sendo 42 pontos em cada área.As determinações da CEa foram realizadas nos meses de outubro, dezembro e maio, nas duas áreas de cultivo utilizado um aparelho portátil modelo LandMapper® ERM 01, também foram coletadas amostras de solo para a determinação de umidade no momento da medição da CEa.Posteriormente os dados foram submetidos a análise de estatística descritiva e geoestatística a fim de verificar a existência e quantificar o grau de dependência espacial.Os resultados demonstram que a utilização de fertilizantes nitrogenados contribuiu para valores de CEa maiores quando comparados a área sem manejo.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.260
Teacher spread0.217 · 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

Citations0
Published2023
Admission routes1
Has abstractno

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