MétaCan
Menu
Back to cohort
Record W2921597117 · doi:10.5539/jas.v11n4p205

New Vigor Methodology in Pearl Millet Seeds

2019· article· en· W2921597117 on OpenAlexvenueno aff
ANNA S. SUÑÉ, Danillo B. Rodrigues, Ana Sofia Almeida, C. A. Nunes, Luis Henrique Konzen, Éwerton Gewehr, G. A. Silveira, Bruna Barreto dos Reis, H. L. Chagas, Lílian Vanussa Madruga de Tunes

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeed Germination and Physiology
Canadian institutionsnot available
FundersDivision of Graduate EducationCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorUniversidade Federal de Pelotas
KeywordsPearlSowingHuskSeedlingAgronomyHorticultureBiologyMathematicsBotanyGeography

Abstract

fetched live from OpenAlex

The use of high quality seeds is of utmost importance and is intimately related to seed vigor. The objective of this study was to stablish a novel methodology for evaluating the vigor of pearl millet seeds. The experiment was performed at the Federal University of Pelotas, using 5 seed lots of pearl millet cv. BRS1501. The research was carried out in two stages: first, seed lots were characterized (physiological quality tests); second, the initial emergence and development of pearl millet seedlings was evaluated, testing four different substrates and five sowing depths in the emergence test. The experimental set up consisted of a completely randomized design with four replicates. Means were compared by the Scott-Knott test at the 5% probability level. The substrates sand and carbonized rice husk, at 1.0 cm depth, satisfactorily stratified seed lots. These methodologies correlated with the speed of emergency index and seedling emergency. Thus, the study identified an innovative method for testing vigor in pearl millet seeds.

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)0.000
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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.293
Teacher spread0.243 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural ScienceSame topicSeed Germination and PhysiologyFrench-language works237,207