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Record W2814132623 · doi:10.5539/jas.v10n8p296

Variation of Seed Dosage on Corn Crop Production Components

2018· article· en· W2814132623 on OpenAlexvenueno aff
Rafael De Graaf Corrêa, Carlos Eduardo Angeli Furlani, Cristiano Zerbato, Danilo Tedesco de Oliveira, Maílson Freire de Oliveira

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsCropProductivityPopulationAgronomyYield (engineering)BiologyCrop yieldZea maysCrop productivityMedicine

Abstract

fetched live from OpenAlex

The variation in population density in a corn crop can positively, negatively or neutrally affects plant productivity depending on the productive potential of the area. The aim of this work was to evaluate the effects of the variation in corn seed dosage on crop yield, and define from which percentage of variation the productivity of the sown line is affected negatively. The experiment was installed at FCAV-UNESP, in Jaboticabal (SP), Brazil. Twelve variations on plant population were evaluated, ranging from -27% to 27%, varying with a frequency of 4.5%. The morphological and productive characteristics of each treatment were evaluated through regression analysis. Each 1% of negative variation on seed dosage was lost 1.06% in corn yield. Positive variations, however, presented changes that were 0 to -2.59%. Negative variations on seed dosage reduced corn productivity by up to 28%. The positive variation affects the productivity of the crop in a less accentuated way, with a reduction in productivity that reaches 2.59% in the largest variations and may even cause positive productivity results depending on the maximum potential of the area.

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)0.001
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 score0.916
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.026
GPT teacher head0.235
Teacher spread0.209 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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