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Record W2961704957 · doi:10.5539/jas.v11n12p29

Seed Size and Its Influence on Growth-Related Agronomic Characters of Wheat Plants

2019· article· en· W2961704957 on OpenAlexvenueno aff
Gustavo Henrique Demari, Ivan Ricardo Carvalho, Vinícius Jardel Szareski, Simone Morgan Dellagostin, João Roberto Pimentel, Cristian Troyjack, Tiago Corazza da Rosa, Velci Queiróz de Souza, Paulo Dejalma Zimmer, Francisco Amaral Villela, Tiago Pedó, Tiago Zanatta

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized block designCultivarSieve (category theory)BiologyAgronomyCropHorticultureMathematics

Abstract

fetched live from OpenAlex

The objective of this work was to evaluate the influence of seed size on agronomic traits related to the growth of wheat plants. The seeds used in this experiment were collected in growing fields from the North of Rio Grande do Sul, Brazil. The experimental design was randomized block design, with three wheat cultivars (Quartzo, Ametista and TBIO Sinuelo) × six seed lots (A; B; C; D; E; F) × five sieve diameters: I: (original sample without standardization, being that used by farmers (OS)), II: (seeds > 3.00 mm), III: (seeds from 2.5 to 2.99 mm), IV: (seeds from 2.0 to 2.49 mm) and V: (seeds < 2.0 mm), and the treatments were arranged in three replicates. The number of tillers per plant, height and spike insertion height are determined by intrinsic characteristics of the cultivar used, but extensive variations are attributed by lot fragmentation and seed size. The morphological and growth attributes of wheat are affected by considerable effects of seed size, and the decrease in these dimensions results in lower plants and reduced growth. The use of sieves for wheat seeds standardization allows the identification and selection of specific cultivars, lots and seed dimensions that may be essential for wheat crop performance.

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 score0.928
Threshold uncertainty score0.155

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 studies0.0000.000
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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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