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Record W2768366958 · doi:10.5539/jas.v9n12p30

Effect of Selected Plant Growth Regulators on Yield and Stem Height of Spring Wheat in Ontario

2017· article· en· W2768366958 on OpenAlexafffundvenueabout
Yuxue Zhang, Shengnan Su, Mirko Tabori, Junjie Yu, Denise Chabot, Bahram Baninasab, Xuelian Wang, B. L., Chunyan Li, Shahrokh Khanizadeh

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaChina Scholarship Council
KeywordsPlant stemChlormequatBiologyYield (engineering)CultivarHorticultureMain stemLimitingPlant growthAgronomyMaterials scienceEngineering

Abstract

fetched live from OpenAlex

Lodging is an important limiting factor in wheat because it affects growth, yield and grain quality. Plant growth regulators (PGRs) are often used to restrain elongation of internodes, improve lodging traits, and protect yield potentials. An experiment was set up in the greenhouse at the Ottawa Research and Development Centre (ORDC) to study the effect of the selected PGRs (Manipulator, the active ingredient of which is chlormequat; and Palisade, the active ingredient of which is trinexapac-ethyl) on yield, stem height and morphological traits in six spring wheat cultivars (AC Carberry, AAC Scotia, Hoffman, Fuzion, FL62R1, and AW725). Both PGRs reduced plant height and caused a 6% to 48% reduction in the length of the second basal internode. The mixture of the two PGRs had a synergistic affect and made the stem shorter. The application of PGRs significantly reduced lodging, increased stem diameter, thickness, filling degree, and stem strength, and increased leaf relative chlorophyll content. However, application of PGRs significantly reduced grain yield, and the combination of the two PGRs (Manipulator and Palisade) had a synergistic effect and lowered the yield. In general, the effect of Palisade was more evident than that of Manipulator.

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 score0.321
Threshold uncertainty score0.993

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 communication0.0000.000
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 head0.015
GPT teacher head0.206
Teacher spread0.191 · 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

Citations20
Published2017
Admission routes4
Has abstractyes

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