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Record W2775035113 · doi:10.5539/jas.v10n1p354

Corn Crop Performance in Different Levels of Defoliation

2017· article· en· W2775035113 on OpenAlexvenueno aff
Wanderley Lulu Gaias, Eduardo Rodrigo Gibbert, Lana Paola da Silva Chidichima, Camila Hendges, Alexandre Muller

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsCropRandomized block designPhenologyAgronomyGrain yieldYield (engineering)Crop yieldHorticultureMathematicsBiology

Abstract

fetched live from OpenAlex

The aim of this work was to evaluate the effect of the yield and morphological components of the maize crop according to different levels of defoliation in the phenological stages of the crop. The experimental design was a randomized block with 7 treatments and 4 replicates. The treatments were: control without defoliation (SD), removal of two leaves (2L-V4) and four leaves (4L-V4) in the V4 stage , removal of the leaves of the lower third (LT-VT), removal of the middle third (MT-VT), removal of the upper third (UT-VT) and total removal (SF-VT) of the leaves in the VT stage. Plant height and spike insertion, number of grains, number of rows, grains per rows, one thousand grain mass and yield were assessed. Plants with total leaf removal had the lowest rates in all aspects evaluated. The number of rows, kernels per row and kernel per spike showed little or no variation in the treatments with partial defoliation. The treatments with removal at the middle and upper third had the lowest values for the analyzed variables when compared to the plants with partial defoliation. The results of this work indicate that the defoliation process can damage the corn yield 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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.040
GPT teacher head0.255
Teacher spread0.215 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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