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Record W2956513692 · doi:10.5539/jas.v11n12p40

Electrical Conductivity Test for Measurement of White Clover Seeds Vigor

2019· article· en· W2956513692 on OpenAlexvenueno aff
Lucian Alex dos Santos, Ivan Ricardo Carvalho, Carolina Cipriano Pinto, Vinícius Jardel Szareski, Jainara Fresinghelli Netto, Letícia Ramon de Medeiros, Andréa Bica Noguez Martins, Nairiane dos Santos Bilhalva, Priscila Monalisa Marchi, João Roberto Pimentel, Cristian Troyjack, Géri Eduardo Meneghello, Lílian Vanussa Madruga de Tunes, Tiago Zanatta Aumonde, Tiago Pedó, Francisco Amaral Villela

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsGerminationOrange (colour)White (mutation)HorticultureBiologyBotanyAgronomy

Abstract

fetched live from OpenAlex

This study aimed to evaluate the physiological performance of white clover seed lots of different tegument colours, besides to analyses the electrical conductivity test methodology with different seeds number, water volume and soaking periods. The experiment was developed at the Seed Didactic Laboratory in the Agronomy College “Eliseu Maciel” at the Federal University of Pelotas, in Pelotas-RS, Brazil. White clover seeds were manually separate, composing four lots of different coloured seeds: yellow, orange, brown and mixed. White clover seeds of yellow colour tend to present greater physiological potential through its germination and vigour. The electrical conductivity test was not efficient on identifying different vigour levels in white clover seed lots.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.024
GPT teacher head0.222
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; 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 designBench or experimental
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

Citations3
Published2019
Admission routes1
Has abstractyes

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