MétaCan
Menu
Back to cohort
Record W2801090948 · doi:10.5539/jas.v10n6p354

Pre-harvest Desiccation: Productivity and Physical and Physiological Inferences on Soybean Seeds During Storage

2018· article· en· W2801090948 on OpenAlexvenueno aff
Elias Zanatta, Vinícius Jardel Szareski, Ivan Ricardo Carvalho, Felipe Koch, João Roberto Pimentel, Cristian Troyjack, Simone Morgan Dellagostin, Gustavo Henrique Demari, Francine Lautenchleger, Velci Queiróz de Souza, Emanuela Garbin Martinazzo, Francisco Amaral Villela, Tiago Pedó, Tiago Zanatta Aumonde

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsGerminationDesiccationPhenologyBiologyProductivityHorticultureAgronomyBotany

Abstract

fetched live from OpenAlex

The objective of this research is to define which soybean phenological stage is adequate to promote pre-harvest desiccation and to measure the effects of this procedure on the physical and physiological attributes of soybean seeds throughout storage. The experiment was carried out at Fazenda Santa Bárbara da Boa Vista located in the municipality of Cabeceiras, Goiás, Brazil. The experimental design was the randomized blocks arranged in a factorial scheme being five phenological stages of soybean development where desiccant was applied (R5.5, R6.0, R7.1, R7.3 and R8.3) × five post-harvest storage times (0, 40, 80, 120, 160 days), arranged in four replicates. The measured characters were: Productivity, Mass of one thousand seeds, Retention of sieves 5.5 mm, 6.0 mm and 6.5 mm, Germination, Accelerated aging and Field emergence. The application of the Paraquat molecule in soybean plants in the phenological stages R5.5 and R6.0 compromises the physical attributes, mass of a thousand seeds and productivity. The germination and vigor of the soybean seeds are adversely affected due to the early desiccation of the plants, and these effects are potentiated throughout the seed storage.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.243
Teacher spread0.216 · 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 teacher head, 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

Citations16
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural ScienceSame topicSoybean genetics and cultivationFrench-language works237,207